Paper Digest: Recent Papers on Question Answering
Paper Digest Team extracted all recent Question Answering related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
Since 2018, Paper Digest has built a foundation of data spanning decades of conferences, journals, and research topics. The platform features a daily digest service that sifts through tens of thousands of new papers, clinical trials, news articles, and community posts, filtering the noise to highlight what matters most to specific interests. Beyond daily updates, dozens of built-in research tools streamline the academic workflow, supporting efficient reading and writing, comprehensive literature reviews, and automated research report generation.
Paper Digest Team
New York City, New York, 10017
team@paperdigest.org
TABLE 1: Paper Digest: Recent Papers on Question Answering
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | Beyond Anonymous Captions: Grounding Character Identity in Video Captioning and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a framework for identity-aware video captioning and person-centric question answering that combines automatic character identification, explicit spatial grounding, and task-specific adaptation. |
ANAS FILALI RAZZOUKI et. al. | arxiv-cs.CV | 2026-10-07 |
| 2 | Cost-Effective Numerical QA Over Semi-Structured Table: Structuring, Resolution, Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose SemiBonsai, a cost-effective, LLM-powered framework featuring: (1) a Multiway Layered Table Structurer that converts a table into a multiway layered tree organized by structural elements; (2) a Context-Aware Uncertainty Resolver that grounds underspecified phrases to context-coherent structural elements; (3) a Plan-Guided Reasoner that decomposes complex query intents into a query plan for reasoning; (4) a Budget-Aware Bandit Router that learns per-instance LLM utilities and optimizes LLM selection under fixed monetary constraints. |
FENG LUO et. al. | arxiv-cs.DB | 2026-10-06 |
| 3 | Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA). |
Yang Hong; Yajun Yang; Xin Wang; Liping Jing; Qinghua Hu; | arxiv-cs.CL | 2026-10-06 |
| 4 | Entity-centric Evaluation of Large Language Model Responses for Medical Question-answering Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing evaluation metrics often fail to capture whether model reasoning preserves patient-specific context and diagnostic intent, while many require external references or manual annotation, limiting scalability and real-world applicability. To address this gap, this study proposes E n t Q A , a reference-free, entity-centric metric that evaluates how well LLM-generated responses retain clinically relevant biomedical concepts from patient backgrounds and diagnostic questions. |
Yi Liu; Vijaya B. Kolachalama; | PLOS Digital Health | 2026-10-06 |
| 5 | The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. |
Jorma Valjakka; Juhani Kivimäki; Juha Mylläri; Jukka K. Nurminen; | arxiv-cs.CL | 2026-10-06 |
| 6 | ChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ChartBmkAgent, which turns an identified capability gap into targeted diagnostic evidence by constructing complete Chart QA samples from sparse error-taxonomy specifications. |
LANGXI HUANG et. al. | arxiv-cs.AI | 2026-10-06 |
| 7 | SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This challenge is exacerbated by stringent privacy requirements and the impracticality of utilizing large open-source corpora or proprietary cloud APIs within resource-limited clinical settings. To address these obstacles, we introduce SAGE (\textit{Semantic Anchor-Guided Evolution}), a novel data synthesis framework that enables small, locally deployed models to generate high-quality medical training data. |
CHUAN LI et. al. | arxiv-cs.CL | 2026-10-06 |
| 8 | Conditional Rank Allocation for Taxonomy-Aware Medical Language Model Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ARBOR, a parameter-efficient method that selects rank-one components from a shared low-rank basis for each question. |
GUANGYUAN DONG et. al. | arxiv-cs.AI | 2026-10-05 |
| 9 | Do Time-Series QA Systems Read The Time Series? Evidence Use and Reasoning Reliability Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on evaluating four time-series QA systems: TimeOmni-1, ChatTS, TimeOmni-VL, and Time-MQA. |
ZHUOMIN CHEN et. al. | arxiv-cs.AI | 2026-10-04 |
| 10 | Zero-Shot Time-Series Question Answering Via Decoupled Perception and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods heavily overfit to specific datasets and struggle to generalize when input series, question contexts, and answer requirements shift simultaneously. To address this challenge, we propose TSHarness, an agentic framework that establishes a decoupled workflow for cross-dataset zero-shot TSQA. |
Jing Xie; Haochen Yuan; Yunbo Wang; | arxiv-cs.AI | 2026-10-04 |
| 11 | Understanding Errors in LLM-Based Question Answering Over Imperfect Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate error discovery and handling in question answering over imperfect tables through controlled studies across three large language models (LLMs) on human-reviewed RADAR-T examples. |
Baowen Zhang; Wei Fan; Ruman Wang; Hangting Ye; | arxiv-cs.CL | 2026-10-03 |
| 12 | A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To reduce it while maintaining retrieval quality, we present MatRAG, a hierarchical framework that combines RAG systems with Matryoshka Representation Learning (MRL). |
GIANLUCA BONIFAZI et. al. | arxiv-cs.CL | 2026-10-01 |
| 13 | Where’s Waldo? Query-language Preference Under Cross-lingual Knowledge Disparities Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this bias becomes consequential when sources in different languages provide incomplete or inconsistent accounts of the same fact, since the information users receive then depends on the sources a model selects to use. To characterize query-language preference under such cross-lingual knowledge disparities, we introduce Waldo, a multilingual Question-Answering (QA) benchmark constructed from Wikipedia. |
Dayeon Ki; Ruochen Zhang; Silviu Cucerzan; Ryen W. White; Ning Gao; | arxiv-cs.CL | 2026-09-30 |
| 14 | Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To help the model learn where to look, we introduce Cardiac Anatomy-Routed Attention (CARA), which selects predicted anatomical priors according to the question and guides decoder attention with learned task-specific strengths. |
BANGWEI GUO et. al. | arxiv-cs.CV | 2026-09-30 |
| 15 | Overview of BioASQ 2026: The Fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. |
ANASTASIOS NENTIDIS et. al. | arxiv-cs.CL | 2026-09-30 |
| 16 | ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce \textbf{ViLegalExpert}, a large-scale benchmark constructed from authentic citizen–lawyer consultations, containing over \textbf{172K} questions across \textbf{34 legal domains}, together with professional answers and expert-verified legal evidence. |
DAT TIEN NGUYEN et. al. | arxiv-cs.CL | 2026-09-30 |
| 17 | RMS-AQA: A Two-Stage Spatial Audio Question Answering Benchmark for Real-World Domestic Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Embodied assistants in domestic environments must infer what happened, where and when it occurred, and how to respond. To address this, we introduce RMS-AQA, a spatial audio question answering (SAQA) benchmark for real-world domestic environments. |
PEIHAO CHEN et. al. | arxiv-cs.SD | 2026-09-30 |
| 18 | Video Evidence Indexing: Learning Where to Look from Video Previews for Token-Budgeted Long-Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A long-video question may require broad temporal coverage, but the answer is often supported by only a compact set of moments. To locate these moments efficiently, we propose token-budgeted Video Evidence Indexing (VEI): given a dense low-resolution Video Preview, the model constructs a compact high-resolution Evidence Set for final reasoning. |
Haowen Guan; Shengzhi Li; Shichao Pei; | arxiv-cs.CV | 2026-09-30 |
| 19 | Lost in Conversation or Lost in Translation? Diagnosing Multi-Turn Degradation in RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented generation (RAG) and its graph-based variant (GraphRAG) have become the dominant approaches for grounding LLM responses in external evidence, yet both are evaluated almost exclusively on single-turn, fully specified queries. We systematically investigate this evaluation mismatch through a large-scale simulation study. |
Pranav Handa; Ariful Azad; | arxiv-cs.CL | 2026-09-29 |
| 20 | Context-engineered Question Answering for Multi-format Construction Safety Documentation Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Bing Shuai; Carlos H. Caldas; | Engineering Applications of Artificial Intelligence | 2026-09-29 |
| 21 | When Capabilities Fail to Compose: Diagnosing The Compositionality Gap in Large Audio-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large audio-language models (LALMs) perform strongly on individual audio tasks, but whether these capabilities can be reliably composed remains underexplored. |
CHIEN-FENG LIU et. al. | arxiv-cs.SD | 2026-09-29 |
| 22 | EnterpriseBench: Benchmarking LLM Agents on Enterprise-Level Strategic Reasoning and Decision-Making Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing enterprise and financial benchmarks mainly test static capabilities such as information extraction, numerical calculation, domain knowledge, and financial QA, leaving interactive and long-horizon decision-making underexplored. To bridge this gap, we introduce EnterpriseBench, a benchmark that evaluates LLM agents across this spectrum, from static question answering to dynamic decision-making. |
MIN YANG et. al. | arxiv-cs.AI | 2026-09-29 |
| 23 | Exemplar2VQA: A Scalable Exemplar-Driven Visual Question Answering Generation Framework Via Multi-Agent Coding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Exemplar2VQA, a scalable exemplar-driven visual question answering generation framework that rapidly synthesizes large-scale spatial QA pairs in simulated environments via multi-agent coding. |
JIAYU YING et. al. | arxiv-cs.CV | 2026-09-29 |
| 24 | TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query Over Time-Series Databases Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmarks fail to adequately capture the non-unified query syntaxes, diverse application domains, and unique time-specific query intents inherent to TSDBs. To address this gap, we introduce TQTS-BENCH, a multi-syntax benchmark for evaluating text-to-query capabilities over TSDBs. |
FEI LYU et. al. | arxiv-cs.CL | 2026-09-28 |
| 25 | From Compression to Execution: What Helps Large Language Models Digest Urban Graphs for Spatial QA? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We construct a controlled multi-task urban graph QA benchmark over three cities that covers adjacency count, adjacency binary, reachability, shortest path, and centrality, and compare seven mechanisms spanning compression, memory, retrieval, execution, and structural reasoning, alongside text-only and GNN-only baselines. |
Rachid Oucheikh; Ali Mansourian; | ISPRS Annals of the Photogrammetry, Remote Sensing and … | 2026-09-28 |
| 26 | Mitigating Hallucinations in Finance-Based Multi-Agent Large Language Model Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this research, we propose a multi-signal framework for hallucination detection and mitigation. |
RASHMI NAGPAL et. al. | Electronics | 2026-09-28 |
| 27 | Signal or Noise? Modality Contribution and Cooperation in Multimodal GraphRAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study how modality-aware retrieval affects downstream inference in a multimodal GraphRAG pipeline, using document visual question answering (DocVQA) as a testbed. |
Antonios Georgakopoulos; Paul Groth; Lise Stork; | arxiv-cs.IR | 2026-09-28 |
| 28 | ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This leaves systematic evaluation of remote sensing video reasoning largely unexplored. To address this gap, we introduce ReVA, a new dataset for remote sensing video question answering, designed to assess spatiotemporal, scene-centric, and reasoning-oriented capabilities of MLLMs. |
ZHEN YAO et. al. | arxiv-cs.CV | 2026-09-28 |
| 29 | Recursive LLM Degradation in Biomedical Question Answering: A Cross-Generation Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Repeatedly training language models on their own generated data may create a synthetic-data feedback loop in which errors and distributional biases are reintroduced into subsequent training datasets. |
Bibek Bhandari; Kshitij Lingthep; | arxiv-cs.CL | 2026-09-28 |
| 30 | Memory in The Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a generative adversarial exam (GAE) that uses forward simulation to evaluate memory retrieval and exam scores to quantify memory quality. |
CHENGYANG LI et. al. | arxiv-cs.RO | 2026-09-28 |
| 31 | SpatialSpeak: QA-Native Reconstruction with Local and Global Context for Spatial Chain-of-Thought Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SpatialSpeak, a two-stage framework that connects QA-native reconstruction pretraining with spatial CoT learning. |
YANG CAO et. al. | arxiv-cs.CV | 2026-09-27 |
| 32 | Dr. Free: You Don’t Need Difficulty Rewards for Self-Evolving Search Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce \methodname, the first self-evolving search framework that eliminates difficulty-based proposer rewards and directly optimizes for evidence necessity relative to shortcut contexts. |
ZHIPENG QIAN et. al. | arxiv-cs.AI | 2026-09-27 |
| 33 | SALMONN-duo: Adaptive Dual-System Coordination for Full-Duplex Voice Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, real-world agents must also use tools and perform deliberative reasoning-operations whose variable latency and computational cost conflict with the stringent timing requirements of real-time conversation. To reconcile these demands, we propose SALMONN-duo, an adaptive dual-system voice agent inspired by dual-process theories of cognition. |
WENYI YU et. al. | arxiv-cs.CL | 2026-09-27 |
| 34 | RCVLA: 4D Radar-Grounded Semantic Reasoning and Trajectory Arbitration for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To support these capabilities, we construct Cap4DR with 86,016 radar-image-text samples for alignment pretraining and OmniHD-QA with 520,161 question-answer pairs for instruction tuning across scene description, key-object reasoning, occupancy understanding, and trajectory planning. Building on these datasets, we propose RCVLA, a radar-camera VLA framework consisting of a radar-grounded semantic reasoning stage (RCVLA-Sem) and a trajectory arbitration stage (RCVLA-Phys). |
LIANQING ZHENG et. al. | arxiv-cs.CV | 2026-09-26 |
| 35 | Delayed Supervision for Test-Time Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluate this approach on LaCT-760M and plain DeltaNet-1.3B using TextWorld training trajectories and shared BABILong and RULER evaluation panels, and include a separately reported RWKV-7 comparison. |
Jinha Kim; Taksh Kothari; | arxiv-cs.AI | 2026-09-26 |
| 36 | BudgetVerify: Budget-Tiered Verification for Financial QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose BudgetVerify, a budget-tiered generator-verifier framework that routes each generated answer to one of three verification tiers: no verification, lightweight check-and-revise, or higher-cost solve-first-then-compare verification. |
Janet Jenq; Hongda Shen; | arxiv-cs.AI | 2026-09-26 |
| 37 | MedRouter: Demystifying Knowledge Differences Across Medical LLMs for Routing-Based Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce MedRouter, an agentic system that uses an embedding-based multi-label router to select and query specialist LLMs, then passes their responses to a generator to produce the final answer. |
Lang Cao; Binghang Lu; Yuhao Shen; Yue Guo; | arxiv-cs.CL | 2026-09-26 |
| 38 | Regime Boundary Alignment for Evidence-Gated Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Regime Boundary Alignment (RBA), which trains a single reader on matched variants of the same question and gold answer. |
ZEYAN LI et. al. | arxiv-cs.CL | 2026-09-25 |
| 39 | CausalDriveBench: Evaluating Causal Reasoning in Vision-Language-Action Models for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CausalDriveBench, an evaluation framework grounded in Pearl’s Causal Hierarchy (PCH) that tests causal reasoning in driving-specific VLAs through structured visual question answering (QA) and alternative-trajectory prediction. |
NARENDIRAN CHEMBU et. al. | arxiv-cs.CV | 2026-09-25 |
| 40 | Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This reveals a critical gap in question answering (QA), where responses must be contextually grounded rather than simply being memorized associations. To fill this void, we propose a novel knowledge graph (KG) based evaluation framework for LLM contextual understanding in QA. |
SUBAVARSHANA ARUMUGAM et. al. | arxiv-cs.AI | 2026-09-24 |
| 41 | Likelihood Ranking Doesn’t Scale Like Prompting in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Across 95 decoder-only models, ranging from 0.1B to 104B parameters, and 10 MCQA datasets, we find a systematic divergence between declarative-statement likelihood ranking and prompted answering. |
Alessandro Bondielli; Lucia Passaro; Davide Bacciu; Alessandro Lenci; | arxiv-cs.CL | 2026-09-24 |
| 42 | TEMA: Evidence-Grounded Temporal Question Answering in Multi-Turn Multi-Audio Dialogs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose TEMA, which connects event perception with evidence-based answering through Route, specifying the audio scope, and Span, describing all relevant intervals as conditional audio captions. |
KAIDI YANG et. al. | arxiv-cs.SD | 2026-09-24 |
| 43 | An Empirical Study of VLM Pipelines for Long-Document QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts. |
Kenan E. Ak; Jay Mohta; Gwang Gook Lee; Yan Xu; Dimitrios Dimitriadis; | arxiv-cs.CL | 2026-09-24 |
| 44 | Cross-Lingual Legal QA for Vietnamese Labour Law: Retrieval, Translation, and Verifier-Guided Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a bilingual evaluation suite of 231 Vietnamese–English question–answer pairs from Vietnamese labour law. |
Nguyen Minh Chi; Mo El-Haj; Nguyen Ha Thanh; Dawn Knight; Paul Rayson; | arxiv-cs.CL | 2026-09-23 |
| 45 | Broadening Uncertainty Estimation for Audio Question Answering Across Methods, Formats, and Inputs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We compare probability-based, sampling-based, self-verification, evidential, and contrastive measures across four open-weight models and five audio QA benchmarks. |
Aaron Isidore Grace; Weiran Wang; | arxiv-cs.SD | 2026-09-23 |
| 46 | CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present CoVLM-Bench, a benchmark for cooperative driving question answering (CDQA) and cooperative planning (CP) on vehicle-infrastructure paired scenes. |
KANG YANG et. al. | arxiv-cs.CV | 2026-09-22 |
| 47 | Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answers into errors. |
DINGYANG LIN et. al. | arxiv-cs.CV | 2026-09-22 |
| 48 | Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present LatWeave, which organizes knowledge into a multidimensional knowledge lattice and compiles multi-hop QA into three deterministic operators — meet (constraint intersection), compare (lattice-order comparison), and abstain (structural abstention); LLMs appear only on the construction side (one-shot extraction) and the query-planning side, while the answer-generation path is zero-LLM, zero-task-training, and auditable end to end — so that question answering over Web-published knowledge becomes reproducible item by item. |
Yuze Ren; Shaoheng Fan; Tao Wang; Yabo Yan; Han Han; | arxiv-cs.CL | 2026-09-22 |
| 49 | Diabetes Question Answering Method Based on Knowledge Graph and Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
YUELONG ZHANG et. al. | Discover Artificial Intelligence | 2026-09-21 |
| 50 | CT-Bench: A Comprehensive Benchmark for Multimodal AI in Computed Tomography Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present CT-Bench, a CT lesion image–text resource and visual question answering benchmark constructed by extending DeepLesion with curated lesion-level text, standardized lesion-size representations, structured attributes, and QA tasks. |
QINGQING ZHU et. al. | Machine Learning for Biomedical Imaging | 2026-09-21 |
| 51 | Intelligent Question-Answering System and Inference Mechanism for Grid Dispatching with Energy Storage Access Scenarios Based on Semantic Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: OBJECTIVES:The research focuses on the field of scheduling, integrating expertise in power dispatching and energy storage grid connection operation with natural language processing technology, and has constructed an efficient question-answering generation framework. |
LIN YANG et. al. | EAI Endorsed Transactions on Energy Web | 2026-09-21 |
| 52 | A$^2$Safe: Counterfactual Evidence-Aligned Adaptive Agent Collaboration for Safe and Effective Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent safety-alignment methods improve refusal behavior and contextual risk awareness, yet correct safety outcomes may still rely on superficial textual or visual correlations, particularly when risk emerges from interactions between individually benign image and question content. To address this issue, we propose A$^2$Safe, a counterfactual evidence-aligned adaptive agent collaboration framework for safe and effective VQA. |
QUANXING XU et. al. | arxiv-cs.CV | 2026-09-21 |
| 53 | E-AVI: Evidence-Grounded Multimodal Assessment for Automated Video Interviews Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present E-AVI, an evidence-grounded framework that extracts timestamped multimodal evidence and integrates dimension-conditioned evidence attention with source-level embeddings for scoring. |
HAOSHEN WANG et. al. | arxiv-cs.AI | 2026-09-17 |
| 54 | Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although large vision-language models (VLMs) provide a promising foundation, their deployment is hindered by the domain shift between standards-derived samples and real-world production-line imagery, together with heterogeneous output spaces spanning choice-based and numerical counting tasks. To address these challenges, we propose a multimodal reasoning framework for cross-domain PCBA visual question answering. |
JIA LI et. al. | arxiv-cs.CV | 2026-09-17 |
| 55 | VākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce VākQA, a Telugu SQA benchmark of 2,001 factoid question-answer pairs across six domains, with 2.53 hours of speech audio, bilingual transcriptions, and human-verified reference answers. |
BHAVANA AKKIRAJU et. al. | arxiv-cs.CL | 2026-09-17 |
| 56 | A Multi-Modal Generative Model for Tomato Disease Leaves Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here, we present SOLAR, a multimodal generative model that understands tomato disease spanning six question-answering tasks. |
Khang Nguyen Quoc; Minh-Phuoc Tran; Gia-Han Truong; Luyl-Da Quach; | arxiv-cs.CV | 2026-09-16 |
| 57 | One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our analysis shows that stronger retrieval generally yields larger gains than increased generation effort, but both exhibit diminishing and non-monotonic returns, indicating that higher-complexity configurations are not uniformly better across queries. Motivated by these findings, we introduce DRAG, a query-adaptive framework for selecting retriever-generator configurations. |
Neeraj Anand; Payel Santra; Partha Basuchowdhuri; Debasis Ganguly; Sumit Bhatia; | arxiv-cs.IR | 2026-09-15 |
| 58 | Diagnosing The Fact-Grounding Gap in Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Multi-hop question answering requires combining information from multiple documents to answer complex questions. These systems have grown increasingly capable, yet when they fail, the error is typically attributed to not finding the right documents. |
Kevin Mo; Nathan Mo; Richard Zhu; | arxiv-cs.CL | 2026-09-15 |
| 59 | Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering Over Scientific Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe tus-nlp’s Lit3R (Retrieve-Relate-Read) system for LitTraceQA, a shared task for literature-grounded question answering that requires systems to retrieve relevant papers, identify supporting evidence, and generate answers. |
AKIRA ISE et. al. | arxiv-cs.CL | 2026-09-15 |
| 60 | A Survey of Visual Question Answering for Embodied Robots: Tasks, Methods, and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Unlike traditional VQA, Embodied Question Answering (EQA) may require an agent to acquire and retain evidence from a 3D environment before answering a natural language query. This semi-systematic survey reviews a core corpus of 72 papers selected from 210 candidates and supplements it with a separate targeted qualitative update comprising 28 additional records identified through July 2026. |
Weihan Shi; Yinlong Liu; | AI | 2026-09-15 |
| 61 | Efficient Reasoning Distillation: Small Video-Language Models Via Synthetic CoT and Difficulty-Aware Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present an efficient method to distill reasoning capabilities into compact video-language models (VLMs) for video question answering (VideoQA). |
Mantek Singh; Jeshwanth Challagundla; Siddharth Raina; Jasmin Jarsania; | arxiv-cs.LG | 2026-09-14 |
| 62 | Vision And Text Transformer For Predicting Answerability On Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Together with our consideration of Answerability in a regression task, we propose VT-Transformer, which exploits visual and textual features through Transformer architecture. |
Tung Le; Huy Tien Nguyen; Le Minh Nguyen; | arxiv-cs.CV | 2026-09-14 |
| 63 | Long-to-Short Video Evidence Reasoning for Grounded Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present LOVER, a \underline{L}ong to sh\underline{O}rt \underline{V}ideo \underline{E}vidence \underline{R}einforced model for grounded question answering (GQA). |
Kaiyan Chen; Junbin Xiao; Xun Yang; | arxiv-cs.CV | 2026-09-14 |
| 64 | Verifiable By Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: An alternative is to ensure that responses are verifiable by construction: providing fine-grained verbatim quotes from reference material that substantiate claims, so users can verify an answer without opening other documents. In this paper, we evaluate the ability of current models to perform this task end-to-end: from providing citations for every factual claim, to producing verbatim quotes, to ensuring that those quotes fully substantiate the claims. |
Jiashuo Zhang; Yuling Chen; Yvonne Commodore-Mensah; Michael Oberst; | arxiv-cs.CL | 2026-09-14 |
| 65 | HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model Via Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The Question – Answering (QA) forums such as Stack Overflow cannot always respond to the questions timely and properly. In this paper, we propose HintMiner, a novel automatic question hints mining tool for users to help them find answers. |
Zhenyu Zhang; JiuDong Yang; | arxiv-cs.LG | 2026-09-13 |
| 66 | Selective Tool Use for Agentic Change Visual Question Answering in Remote Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although vision language models (VLMs) have shown promising performance on this task, they remain unreliable when answering questions that require explicit transition statistics, area measurements, or spatial information. To address this limitation, we propose a selective tool use framework in which a single VLM either answers directly or invokes a deterministic change analysis tool to obtain question specific evidence. |
Yakoub Bazi; Mohamad M. Al Rahhal; Mohamed A. Mekhtiche; Mansour Zuair; | arxiv-cs.CV | 2026-09-13 |
| 67 | HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a complex question are usually connected through intermediate entities, relations, and constraints. We propose HyperProve, a retrieval-augmented QA framework that addresses this challenge by coupling question decomposition with answer-conditioned expansion over a hypergraph of atomic facts. |
AN NGUYEN PHU et. al. | arxiv-cs.CL | 2026-09-12 |
| 68 | T-SMART: Mechanism-Level Attribution for Tool-Augmented Time-Series Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Tool-augmented approaches improve performance, but existing systems often intertwine language reasoning, computation, and perception, making it difficult to determine which components drive the gains. We present T-SMART, a neurosymbolic framework that separates these roles: a frozen LLM interprets questions and selects operations, deterministic tools perform numerical computation, and structured perception is invoked only when needed. |
IVAN DELGADO et. al. | arxiv-cs.LG | 2026-09-12 |
| 69 | DoCoG: Mask-based Multi-Type Grounded Chain-of-Thought for Document QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present DoCoG, a mask-based multi-typegrounded step-wise Chain-of-Thought architecture for Document QA.The model supports multi-type grounding, i.e., it localizes both textualcontent and fine-grained graphical elements such as legends, pie slices,and flowchart nodes. |
SAI MADHUSUDAN GUNDA et. al. | eccv | 2026-09-10 |
| 70 | MotionChain: Fine-Grained Video Motion Understanding Via Structured Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, when answering motion-centric questions, VLMs tend to introduce motion-irrelevant contextual information and lack a structured and precise representation of motions, making it difficult to distinguish what motion the subject performs and when. To address these problems, we propose MotionChain, a training-free method that decouples motion question answering into a sequence of ⟨time range, subject, motion, environment⟩ tuples. |
HAO YANG et. al. | eccv | 2026-09-10 |
| 71 | TimelyRAG: Semantic-Temporal Hybrid Retrieval for Time-Critical Question Answering in Overlapping-Evolving Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose TimelyRAG, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. |
YOUNGEUN NAM et. al. | arxiv-cs.IR | 2026-09-10 |
| 72 | A Knowledge Graph-Augmented Large Language Model Framework for Context-Aware Question-Answering and Intelligent Feedback Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study proposes EQAS (Empowered Question-Answering System), a hybrid framework designed to support context-aware question-answering and intelligent feedback generation in domain-specific knowledge environments. |
Bihter Das; Pınar Gokcimen; Tunahan Gokcimen; Muzeyyen Bulut Ozek; | Applied Sciences | 2026-09-10 |
| 73 | ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This perception challengefundamentally differs from static image text understanding, yet existingdatasets fail to capture: the vast majority of questions remain answerablefrom single frames, inadequately reflecting real-world video text compre-hension demands. To address this, we present ViTexQA, a large-scalevideo-text QA dataset, and FrameThinker for robust multi-frame tem-poral reasoning. |
ZHENTAO GUO et. al. | eccv | 2026-09-10 |
| 74 | Evidence-Backed Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Evidence-Backed Video Question Answering (E-VQA), a novel task requiring models to jointly output a semantic answer and precise spatio-temporal evidence: temporal segments and dense, tracked object segmentation masklets. |
SHIJIE WANG et. al. | eccv | 2026-09-10 |
| 75 | ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ReQuest, an uncertainty-driven, question-adaptive keyframe selection pipelinethat aligns question intent with relevant video content through selec-tive computation. |
MINKUK KIM et. al. | eccv | 2026-09-10 |
| 76 | Memory Tree Guided Key Frame Querying for Efficient 3D Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, wepropose a memory tree guided key frame selection paradigm for efficient3D question answering in embodied scenarios. |
HSIANG-WEI HUANG et. al. | eccv | 2026-09-10 |
| 77 | ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ProMSA, a progressive multimodal search agentfor KB-VQA. |
ZHENGXIAN WU et. al. | eccv | 2026-09-10 |
| 78 | Evaluation of Contextual Understanding in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel knowledge graph-based evaluation framework introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating structural and semantic similarity into a continuous evaluation score, alongside a diagnostic framework for categorizing reasoning errors. |
SUBAVARSHANA ARUMUGAM et. al. | arxiv-cs.CL | 2026-09-08 |
| 79 | Application of Knowledge Graph-Based GraphRAG in Intelligent Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper adopts a literature review approach to survey knowledge graph-enhanced GraphRAG. |
Fanhao Zhou; | Applied and Computational Engineering | 2026-09-08 |
| 80 | Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a comprehensive methodological comparison of three influential frameworks, namely Multimodal Adaptive Extraction (MAE), Solar, and UniMMQA, tracing the evolution of multimodal question answering from modality-adaptive pipelines to fully unified architectures. |
Abdullah Al Shafi; | arxiv-cs.CL | 2026-09-08 |
| 81 | When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce \emph{neutral-conditioned misleading cue adoption rate} (NC-MCAR), which measures switches to that option only on valid cued trials where the same model first selected the gold answer under a neutral prompt. |
Manikandan Ravikiran; Siddharth Vohra; | arxiv-cs.CL | 2026-09-08 |
| 82 | Which Medical Questions Deserve Rationales? Perturbation-Sensitive Selection for Robust QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose root-mean-square Robustness-based Sample Prioritization (RMS-RSP), which perturbs hidden states only at rationale tokens and measures the resulting shift in the gold-versus-best-distractor margin. |
Yuexin Wu; Dayou Yu; Vasile Rus; | arxiv-cs.CL | 2026-09-08 |
| 83 | Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods suffer from two key challenges: (1) fragmented evidence scattered across temporally distant sessions, and (2) noisy content within retrieved sessions that triggers the lost-in-the-middle effect. To address these challenges, we propose MemLoc, a unified Retrieve-Localize-Generate framework for long-term conversational memory QA. |
YIFAN WANG et. al. | arxiv-cs.CL | 2026-09-07 |
| 84 | Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. |
JungMin Yun; YoungBin Kim; | arxiv-cs.AI | 2026-09-07 |
| 85 | Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence Analysis with A Knowledge-Enabled Agent for Colorectal Cancer Diagnostics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here, we present Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence Analysis with a Knowledge-Enabled Agent (MADAK-Agent), an integrated analytical platform that combines an Argonaute-mediated cascade amplification module with a monodisperse droplet-assisted, object-resolved post-amplification fluorescence measurement workflow and a knowledge-enabled interpretation interface. |
HANXUE LI et. al. | Analytical Chemistry | 2026-09-07 |
| 86 | IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. |
Yuwen Chiu; | arxiv-cs.CL | 2026-09-07 |
| 87 | HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing medical wearable question answering (QA) benchmarks primarily assess short-horizon classification or statistical summaries, largely ignoring the long-term patterns, therapeutic and behavioural contexts, and potential system failures inherent in real-world deployments. To address this, we introduce HealthLoopQA, a comprehensive diagnostic benchmark for evaluating LLM reasoning over continuous diabetes monitoring data. |
YUCHEN NIU et. al. | arxiv-cs.LG | 2026-09-06 |
| 88 | Companion-style QA Assistance in Ego-Vision Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companion-style question answering (QA) on egocentric streaming video. |
Hangyu Qin; Junbin Xiao; Shenglang Zhang; Angela Yao; | arxiv-cs.CV | 2026-09-06 |
| 89 | CAM: Question Answering on Entity-Centric Videos with Continuous Extraction and Adaptive Querying Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, they rely solely on similarity-based retrieval and may fail to retrieve the fine-grained details required for question answering. To tackle these problems, we propose CAM, featuring continuous extraction for high-level semantics and adaptive querying for fine-grained details. |
YIZHOU TIAN et. al. | arxiv-cs.CV | 2026-09-06 |
| 90 | Systematic Review and Comparative Synthesis of Cross Lingual Question Answering Systems for Low Resource Indic Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Siddesh Savant; Uttam U. Deshpande; Jovi D’Silva; | Discover Artificial Intelligence | 2026-09-06 |
| 91 | STQA: A Benchmark for Stock-Focused Tabular Question Answering Over Historical and Forecasted Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Built on a large-scale financial dataset, STQA covers 4,417 stocks and contains 31,400 question-answer pairs derived from expert-crafted templates, accompanied by fine-grained intent and slot annotations. To operationalize this benchmark, we present SQFRS (Stock Query-Forecast-Reasoning System), an agent-based unified framework that orchestrates SQL retrieval and time-series forecasting tools. |
Baoxu An; Wenmian Yang; Zhensheng Wang; Weijia Jia; | arxiv-cs.CL | 2026-09-05 |
| 92 | Don’t Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for Multi-hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of decomposition improve the pipeline, but we show that both share the same vulnerability, the loss of entity information, and that this loss breaks the pipeline at two separate points. |
Heechang Lee; Dong-Young Lim; | arxiv-cs.CL | 2026-09-05 |
| 93 | MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the development of clinically reliable and linguistically inclusive medical AI systems remains a significant challenge, primarily due to the lack of multimodal, multilingual, and time-series-grounded benchmarks that reflect the complexity of real-world clinical scenarios. To fill this gap, we present MMTClinic, a benchmark designed to evaluate large language models (LLMs) on complex reasoning and question-answering tasks involving clinical time-series. |
SOURAV MALAKAR et. al. | arxiv-cs.CL | 2026-09-04 |
| 94 | A Tree-based RAG Framework for Evidence-Intensive QA Via Adaptive Planning and Topology-Aware Evidence Gathering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they face key limitations in evidence-intensive QA, where answering a question requires synthesizing information scattered across dozens or even hundreds of documents: structural rigidity, which limits adaptive reasoning expansion, and topology-ignorant evidence gathering, which prevents effective integration of evidence across different reasoning nodes. To address these issues, we propose APT-RAG, an Adaptive Planning and Topology-aware evidence gathering RAG framework. |
SONGEUN LEE et. al. | arxiv-cs.AI | 2026-09-04 |
| 95 | TabScope: Question-Adaptive Scope Selection for Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Localization-sensitive questions are particularly affected by irrelevant table content, while questions requiring broader evidence may still benefit from full-table reasoning. Based on this observation, we propose a question-adaptive framework that dynamically selects between localized and full-table reasoning. |
Yuxiang Wang; Junhao Gan; Jianzhong Qi; | arxiv-cs.CL | 2026-09-03 |
| 96 | When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model responses, but its effect is not uniformly beneficial in single-turn mental-health question answering, where user queries often combine emotional distress, treatment concerns, and safety-sensitive needs. We study when retrieval helps or hurts mental-health QA, and whether a lightweight selective retrieval policy can better control this trade-off. |
Hyunseo Oh; Chong-Kwon Kim; Yoonhyuk Choi; | arxiv-cs.CL | 2026-09-03 |
| 97 | Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks’ Financial Statements Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce FinRAG-QA, a novel benchmark dataset for financial question answering, which comprises 999 practitioner-curated questions on 10 standardised indicators, grounded in 209 annual and Pillar 3 reports from 24 major European and U.S. banks spanning 2019-2023. |
Arianna Miola; Bruno Spaccavento; Lorenzo Silotto; Marco Bianchetti; Luca Cagliero; | arxiv-cs.CL | 2026-09-03 |
| 98 | PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge and clinical reasoning in large language models (LLMs) and large vision-language models (LVLMs). |
TAEGYUN KIM et. al. | arxiv-cs.CL | 2026-09-03 |
| 99 | KhatianDoc: A Human-Verified Benchmark Diagnosing Multimodal LLM Failure on Bengali Legal Land Records Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce KhatianDoc, a four-task benchmark built from 107 real RS Khatian records from the Vumi (land) Office of Munshiganj, Bangladesh: symbol recognition, base-16-to-decimal conversion, structured field extraction, and legal document question answering over 1,634 QA pairs. |
TASMIAD HASAN et. al. | arxiv-cs.CL | 2026-09-03 |
| 100 | Gate2Code: Difficulty-Aware Planning and Code Generation for Arabic Tabular Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Gate2Code, a difficulty-aware pipeline for Arabic Tabular QA combining three components: (i) LLM-based difficulty routing, (ii) Arabic-aware table normalization, and (iii) hybrid function-level code generation with controlled fallback execution. |
Rana Alshaikh; | Electronics | 2026-09-03 |
| 101 | The Bairong System for MLC-SLM 2026: Dynamic Question-Aware Evidence Routing for Multilingual Conversational Speech Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present the Bairong system for the MLC-SLM 2026 Challenge, where a diarization-ASR front-end produces speaker-attributed transcripts and a dynamic evidence router constructs question-specific inputs for answer prediction. |
SHANGKUN HUANG et. al. | arxiv-cs.CL | 2026-09-02 |
| 102 | PowerMTQA: Dual-Stream Multimodal Temporal Question Answering for Power-System Customer Service Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose PowerMTQA, a dual-stream multimodal temporal question-answering framework that combines a Qwen-based visual-language backbone with a dedicated multichannel time-series branch, prototype reprogramming, explicit cross-attention fusion, and auxiliary anomaly/risk supervision. |
Guorui Wang; Jiang Jiang; Jinchen Ma; Dingxiao Liu; | Tsinghua Science and Technology | 2026-09-01 |
| 103 | Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Evidence Sufficiency Boundary Training, a generation-native training framework that constructs ordered evidence chains and supervises the abstain-to-answer transition directly. |
Haruto Sato; Yuki Tanaka; Ren Nakamura; Aoi Kobayashi; Mei Ito; | arxiv-cs.CL | 2026-09-01 |
| 104 | Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Probing four open-weight LLMs (2–8B), we find that the truth direction is largely robust to writing style (mean $Δ_\text{register} \approx 0.10$ AUROC on held-out facts) and to medical specialty ($Δ_\text{specialty} \approx 0.03$), but degrades unevenly across corpora: by $0.12$ AUROC on MMLU-medical and by $0.21$ on MedMCQA, roughly twice the register gap. |
Nishant Mishra; Ameen Abu-Hanna; Iacer Calixto; | arxiv-cs.CL | 2026-09-01 |
| 105 | How Correct Is Your Answer? A Semantic Correctness Framework for Open QA Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing judgment-based and similarity-based metrics often collapse these distinctions. We address this gap with three reusable contributions. First, we introduce a semantic correctness taxonomy that assigns open-ended answers to eight ordered classes, separating verbose-but-correct answers from those contaminated by hallucinated content. |
ELITSA YOTKOVA et. al. | arxiv-cs.CL | 2026-09-01 |
| 106 | Multi-Agent Collaborative Knowledge Question Answering for Mineralogy Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
ZHANHAO ZHANG et. al. | Artificial Intelligence in Geosciences | 2026-09-01 |
| 107 | EDRAC: Benchmarking Arabic Dialect Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing Arabic QA benchmarks primarily focus on formal written MSA or multiple-choice QA, with limited coverage of naturally spoken dialects. Here, we aim to bridge this gap. |
NOOR ABO MOKH et. al. | arxiv-cs.CL | 2026-09-01 |
| 108 | Staged Linguistic Seeding: Grounded Query Expansion for Verified-Unit QA in AI Contact Centers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Verified-unit answering also removes free-form generation’s unsupported-content surface (7-13% versus approximately 0%). We report this as an application study, including negative results. |
Hyeonseop Yoon; Jeong-Eun Park; | arxiv-cs.CL | 2026-09-01 |
| 109 | EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the Evidence-Grounded Typed Knowledge Graph (EGT-KG), a retrieval framework to improve information retrieval with local SLMs. |
MURAN YU et. al. | arxiv-cs.AI | 2026-08-31 |
| 110 | Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods address this mismatch by imposing fixed graph structures over the corpus, by iteratively reformulating the query, or by executing a generated program over it, but these strategies do not explicitly decide when a query unit is already supported by evidence and when it should be refined. We formulate this bottleneck as retrievable granularity discovery and introduce Hi-Q, an evidence-conditioned framework for hierarchical query refinement. |
Jueun Kim; Sungho Park; Wook-Shin Han; | arxiv-cs.CL | 2026-08-31 |
| 111 | Do Small Models Use The Law You Give Them? Measuring Context Use on A Bilingual Bangladesh Legal Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluate six instruction-tuned models: Llama-3.2-1B, Llama-3.2-3B, Qwen3.5-0.8B, Qwen3.5-2B, Qwen3.5-4B, and Gemma-4-E2B, with three LoRA seeds per model. |
MONIRUZZAMAN MAHADI et. al. | arxiv-cs.CL | 2026-08-31 |
| 112 | ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Thus, we propose ISO-RAG (ISOperimetric Retrieval-Augmented Generation), a geometry-aware RAG framework. |
Siyuan Zhang; Hanchen Wang; Dong Wen; Ying Zhang; Wenjie Zhang; | arxiv-cs.AI | 2026-08-31 |
| 113 | DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we explore the value of agentic restoration for real-world degraded document VQA and propose DocIntent, a training-free Answerability-Guided Agentic Restoration framework. |
ZIHAN HUANG et. al. | arxiv-cs.CV | 2026-08-29 |
| 114 | A Visual Question Answering Model to Automate Nondestructive Evaluation Image Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study introduces a Visual Question Answering model designed specifically for nondestructive evaluation applications. |
Mehrdad Shafiei Dizaji; Hoda Azari; | arxiv-cs.CV | 2026-08-29 |
| 115 | HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present HANIA, a planner-guided multimodal graph framework for evidence-grounded question answering. |
ZAFAR ALI et. al. | arxiv-cs.AI | 2026-08-29 |
| 116 | Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering Over Revised Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present ingest-time fact compilation, an architecture that performs this work when corpus data is ingested or changed. |
Kyle Wild; Yusuke Takahashi; Asako Uraki; | arxiv-cs.AI | 2026-08-29 |
| 117 | Blind Men and The Elephant: Probing The Epistemic Myopia of LLMs Under Long-Tail Divergent Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Factual question answering (QA) typically assumes a single canonical answer, obscuring whether large language models (LLMs) retain divergent accounts of long-tail facts. To address this gap, we introduce ElephantBench, a closed-book knowledge probe comprising 1,094 questions generated through an auditable graph-based pipeline. |
ZHUOSHI PAN et. al. | arxiv-cs.CL | 2026-08-28 |
| 118 | BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: But they remain very unexplored for low-resource languages like Bangla due to limited datasets and systems tailored to these languages. To address this, we introduce BanglaMed-QA, a robust QA system specifically designed for the Bangla medical domain. |
Rowzatul Zannat; Abdullah Al Shafi; K. M. Azharul Hasan; Atia Shahnaz Ipa; | arxiv-cs.CL | 2026-08-28 |
| 119 | Inter-3D VQA: A Roadside Multimodal Benchmark for 3D Spatiotemporally Grounded Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Inter-3D VQA, a large-scale roadside multimodal benchmark for 3D spatiotemporally grounded VQA at intersections. |
Shaozu Ding; Linan Song; Dajiang Suo; | arxiv-cs.CV | 2026-08-28 |
| 120 | Propagating Construction-time Knowledge Quality Into Medical Question Answering: A Framework Grounded in Clinical Guidelines Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate whether construction-time triple quality can serve as a persistent signal for downstream evidence selection and presentation. We propose a quality-aware framework that models structural conformance (SchemaConf) and evidential support (EvidScore) as complementary dimensions and fuses them into a per-triple quality signal, Q(t). |
JIE HU et. al. | arxiv-cs.AI | 2026-08-28 |
| 121 | Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements Without Accuracy Drops Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Models (LLMs) often answer the same factual question differently across languages. |
Faeze Ghorbanpour; Constanza Fierro; Alexander Fraser; Anders Sogaard; | arxiv-cs.CL | 2026-08-28 |
| 122 | From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a comprehensive pipeline for improving financial QA systems through high-quality synthetic data generation and fine-tuning of smaller language models (SLMs) using Quantized Low-Rank Adaptation (QLoRA). |
Lokendra Birla; Milind Savagaonkar; Visnu Srinivasan; Sowmya Rasipuram; Shubhashis Sengupta; | arxiv-cs.AI | 2026-08-28 |
| 123 | TabuLM: Morphology-Aware Tabular Pre-training for Low-Resource Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present TabuLM, the first language model pre-trained on Kinyarwanda tabular data. |
Ireddi Rakshitha; Devavarapu Yashwanth; Ntakirutimana Pierre; | arxiv-cs.CL | 2026-08-27 |
| 124 | PROVE: Proof-guided Regime-aware Operator Verification for Hallucination Detection in Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present PROVE (Proof-guided Regime-aware Operator Verification), a black-box detector that adapts verification strategy to the evidential structure of each question. |
KEYANG ZHOU et. al. | arxiv-cs.CV | 2026-08-27 |
| 125 | Towards Expert Financial QA Via Self-Improving RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes document QA into three specialized agents (Retrieval, Reasoning, and Judge) coordinated by an orchestrator with feedback-driven self-correction. |
Junjie Xiong; Shawheen Ghezavat; Aum Hirpara; | arxiv-cs.CL | 2026-08-27 |
| 126 | A Table Is Worth 64 Tokens: Pixel-level Compression for Multi-Table Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study pixel-level table compression for question answering over documents with multiple tables, evaluating five VLMs across two benchmarks and five visual-token budgets. |
Iñigo Alonso; Mirella Lapata; | arxiv-cs.AI | 2026-08-27 |
| 127 | Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent efforts on decentralized RAG primarily follow prompt-based paradigms that exchange raw, human-readable retrieved content, leading to substantial inference-time computational overhead and direct exposure of retrieved information. To address these limitations, we propose Representation-based Federated RAG (FedRepRAG), a decentralized RAG framework that keeps raw documents at their owning clients and exchanges only compact latent representations during cross-client retrieval. |
CAN PENG et. al. | arxiv-cs.CL | 2026-08-26 |
| 128 | ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation Via Reliability-Guided Reasoning Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing approaches mainly rely on implicit alignment or explicit regulation, but their limited ability to assess fine-grained information reliability makes them vulnerable to deceptive misinformation that is semantically relevant to the question yet factually incorrect, leading to erroneous answers. To address this limitation, we propose ReliableRAG, which, to the best of our knowledge, is the first reliability-driven framework that mitigates deceptive misinformation in multi-hop QA through fine-grained evaluation of individual triples. |
JINPU JIANG et. al. | arxiv-cs.CL | 2026-08-26 |
| 129 | When RAG Fails to Equalize: Geo-bias in Factual Question Answering Over Public Companies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented generation (RAG) is widely assumed to mitigate factual errors in large language models (LLMs), but it remains unclear whether retrieval uniformly compensates for missing knowledge. We study this question in a controlled factual QA setting over public companies, constructing a benchmark of approximately 2,000 firms across global equity indices. |
Abhinav Havaldar; Enrico Santus; | arxiv-cs.CL | 2026-08-26 |
| 130 | Co-Evolving Structured Knowledge and Reasoning in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Structured knowledge bases offer a more controllable alternative, yet they are expensive to construct and often brittle to reason over. To address these limitations, we propose KBevo: a co-evolving framework that jointly learns to construct a structured knowledge base and reason over it for knowledge-intensive question answering. |
RYAN THOMAS NOONAN et. al. | arxiv-cs.CL | 2026-08-26 |
| 131 | SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modalities provide diverse … |
YAOJUN HU et. al. | arxiv-cs.CV | 2026-08-26 |
| 132 | LivingRAG: Augmenting Graph RAG with Experience Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose LivingRAG, a Graph RAG framework with writable and reusable reasoning experience. |
Yuzhuo Cui; Zongye Zhang; Qingjie Liu; | arxiv-cs.AI | 2026-08-26 |
| 133 | DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While existing repository understanding methods have advanced the field, they predominantly rely on surface-level code retrieval and lack the ability for deep reasoning over multiple files, complex software architectures, and grounding answers in long-range code dependencies. To address these limitations, we propose DeepRepoQA, a novel question answering (QA) framework for repository-level code understanding. |
WEIHAN PENG et. al. | arxiv-cs.SE | 2026-08-25 |
| 134 | Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study whether a frozen verifier can also guide training. |
MINGWEN ZHANG et. al. | arxiv-cs.CV | 2026-08-25 |
| 135 | Constrained Entity Selection Under Partial Knowledge for LLM-Based Knowledge Graph QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study a complementary setting in which \emph{candidate} answers are generated by an LLM-based system and subsequently verified using lightweight symbolic constraints derived from the question. |
Emanuel Kitzelmann; | arxiv-cs.AI | 2026-08-25 |
| 136 | Don’t Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing speech QA paradigms and state-of-the-art Speech LLMs suffer from acoustic information loss and poor long-term context memory. To address these issues, we construct the LongAudioQA dataset and propose the GRGA model, which models heterogeneous audio features into a multi-dimensional graph and leverages agent planning for retrieval and answer generation. |
Quanwei Tang; Dong Zhang; Shoushan Li; Guodong Zhou; | arxiv-cs.SD | 2026-08-25 |
| 137 | SelfGraphRAG: Bridging The Supervision Gap in Graph-Based RAG with Synthetic QA Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Graph-based RAG can capture entity relationships, yet supervised graph retrieval typically requires labeled question-answer data that may not be available for newly constructed graphs. We address this limitation with SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever. |
Ben Lagnese; Manas Gaur; | arxiv-cs.CL | 2026-08-25 |
| 138 | A Visual Question Answering Model Based on Entity Knowledge Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Kun Zhu; Kun Zhou; Dexin Zhao; | Multimedia Systems | 2026-08-24 |
| 139 | OVIBench: Benchmarking Online Video Question Answering Under Interruption Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing video QA research and benchmarks still follow an offline, single-round paradigm, overlooking realistic interactions where users may interrupt the model during answer generation. To address this gap, we formulate the task of Online Video Question Answering under Interruption and introduce OVIBench, the first standardized benchmark for evaluating VLMs in this setting. |
NAIMING LIU et. al. | arxiv-cs.CV | 2026-08-23 |
| 140 | GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present \textsc{GTA-RAG}, a graph-trajectory-augmented RL framework for multi-turn retrieval-augmented reasoning. |
JUN CHEN et. al. | arxiv-cs.CL | 2026-08-23 |
| 141 | GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability Through Selective Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In such domains, a confidently wrong answer carries greater risk than abstaining. We present GeoRisk-RAG, a novel hierarchy-aware framework that addresses this geographic-validity gap through selective answering. |
Meenu Ravi; Shailik Sarkar; Lulwah AlKulaib; Yordanos Tessema; Chang-Tien Lu; | arxiv-cs.CL | 2026-08-23 |
| 142 | SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose SSE-Bio, a structured self-evolving agent with an agentic retrieval policy for multi-hop biomedical reasoning. |
ZHAOHAN MENG et. al. | arxiv-cs.CL | 2026-08-22 |
| 143 | Automating Multi-Hop RAG Evaluation Via TRIAD: From Context Extraction to Validated Dataset Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces TRIAD, a three-stage automated dataset generation approach. |
Lorenz Brehme; Adam Jatowt; | arxiv-cs.CL | 2026-08-21 |
| 144 | EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index. |
Xuanyu Meng; Jiashuo Sun; Jash Rajesh Parekh; Jiawei Han; | arxiv-cs.CL | 2026-08-21 |
| 145 | Enhancing LLMs in Predictive Political QA with Semi-Structured Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose PSL, a dual-view framework that converts semi-structured political records into inference-oriented evidence for LLMs. |
YINAN LIU et. al. | arxiv-cs.AI | 2026-08-21 |
| 146 | PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. |
Srikar Kashyap Pulipaka; | arxiv-cs.CL | 2026-08-21 |
| 147 | Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose IAR (Inject, Align, and Recover), a three-stage post-training framework that separates structured document knowledge injection, QA behavior alignment, and general ability recovery. |
Qian Kou; Xiaofeng Shi; Xiaosong Qiu; Hua Zhou; | arxiv-cs.CL | 2026-08-20 |
| 148 | SABET-QA: Temporal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose SABET-QA, a framework that iteratively refines reasoning states across multiple hops via a bidirectional entity-temporal scoring mechanism and a slot-aware contextualization module that aligns question semantics with temporal KG embeddings. |
Brahim Touayouch; Mirette Moawad; Dmitry Akulov; | arxiv-cs.CL | 2026-08-20 |
| 149 | Artificial Intelligence-Driven Sensing of Cross-Border Trade Risks Through Declaration-to-Physical-Fact Alignment and Evidence-Grounded Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Cross-border trade security risks are often embedded in inconsistencies among trade documents, logistics trajectories, hardware sensor states, and financial settlement activities. … |
MEITONG CHEN et. al. | Sensors | 2026-08-20 |
| 150 | DeepWeaver: Bridging The Evidence Synthesis Gap in Open-Ended Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Thus, we propose DeepWeaver, a novel framework that weaves noisy retrieved evidence into comprehensive answers by maintaining Thought Block Chains (TBCs), a structured representation that groups claims, salient information, keywords, and supporting evidence. |
Xujia Wang; Yizhe Zhang; Bin Xu; Lei Hou; Juanzi Li; | arxiv-cs.CL | 2026-08-19 |
| 151 | An AI-powered Knowledge Graph-based Question Answering System for Charak Samhita: Integrating Sanskrit NLP and Graph Data Science Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study utilizes knowledge graph (KG) technology to develop a KG model for an Ayurveda question-and-answer system. |
Sharayu Mirasdar; Mangesh Bedekar; | International Journal of Informatics and Communication … | 2026-08-19 |
| 152 | Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work introduces an adaptive memory and reflection (AMR) agentic system, a multi-agent framework in which specialized agents use dedicated memory and reflection-based feedback to retrieve relevant prior cases and improve subsequent reasoning. |
Pradeep Murugesan; Luoxiao Yang; Xueli Chen; Xinqi Fan; | arxiv-cs.AI | 2026-08-19 |
| 153 | PROBE: Manipulation-Grounded Visual Question Answering with VLM Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Answering such questions in real-world cluttered environments requires reasoning in dynamic scenes: distractors must be manipulated to reveal occluded objects, and each action changes the scene the model must reason over. We formalize this setting as Manipulation-Grounded Visual Question Answering (MG-VQA) and introduce PROBE, a framework for benchmarking and finetuning VLM agents on such tasks. |
VINEET BHAT et. al. | arxiv-cs.CV | 2026-08-17 |
| 154 | Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This deceptive hallucination arises because timestamps imply grounding without ensuring correctness, increasing user trust but not accuracy. We introduce a pipeline that closes this loop. |
Yogesh Kumar; | arxiv-cs.CV | 2026-08-16 |
| 155 | HyLoVQA: Dynamic Hypernetwork-Generated Low-Rank Adaptation for Continual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This often leads to cross-level task interference, hindering accurate adaptation to the current task and object. To address this limitation, we propose HyLoVQA. |
YIRAN WANG et. al. | ijcai | 2026-08-15 |
| 156 | From Time Series Analysis to Question Answering: A Survey in The LLM Era Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities to support tasks such as forecasting and anomaly detection. |
WEI LI et. al. | ijcai | 2026-08-15 |
| 157 | Benchmarking Real-Time Question Answering Via Executable Code Workflows Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing benchmarks are predominantly static and therefore fail to capture the temporal dynamics of information and the continuously evolving nature of real world knowledge. To address this limitation, we propose RT-QA, a dynamic evaluation framework that leverages executable code workflows to retrieve current answers at evaluation time. |
WENJIE ZHOU et. al. | ijcai | 2026-08-15 |
| 158 | NaVQA: Mitigating Silent Failures in Question Answering Over Virtual Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we study the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries. |
GUOHUI XIAO et. al. | ijcai | 2026-08-15 |
| 159 | Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a reproducible measurement framework that treats hallucinations, unsupported statements in document-grounded QA, as diagnostic signals of ideological drift. |
Chendi Wang; Liam Cunningham; Tom Yishay; Jieying Chen; | ijcai | 2026-08-15 |
| 160 | Towards Building A Multi-Source Heterogeneous Knowledge Graph for Complex Material Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, general graph-based retrieval does not necessarily preserve the hierarchical relations and factual attributes required to resolve implicit material constraints. To address these limitations, we propose MCTD-KG, a multi-source heterogeneous knowledge graph integrated with a Knowledge-Enhanced RAG framework for complex material question answering. |
PEIZE LI et. al. | Electronics | 2026-08-14 |
| 161 | Can Language Models Understand MmWave Data? Benchmarking Large Language Models for MmWave Radar-Based Human Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We address this gap through a minimal textualization interface that serializes each mmWave point cloud into concise natural language, allowing off-the-shelf LLMs to operate in a question answering (QA) setting. Building on this, we present mmWave-QA, the first benchmark for language-conditioned mmWave human perception. |
Jeongwan Shin; Jaehyeon Kim; Donguk Ko; Jaeho Choi; | arxiv-cs.AI | 2026-08-14 |
| 162 | Assessing Reliability of BERT-based Models on Question Answering Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Pooja Yadav; Priyanka Harjule; Basant Agarwal; Marko Robnik Šikonja; | Journal of Experimental & Theoretical Artificial … | 2026-08-14 |
| 163 | What The Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Trident, with two complementary components: Trident-R, a retriever-agnostic LLM reranker that converts each candidate into an LLM-readable semantic record, including a visual caption, section path, entity tags, multi-axis concept hits, and a text snippet, then performs a single adaptive-K rerank call; and Trident-S, a generation-side module that prompts the VLM under topical, entity, and structural lenses before synthesis. |
Guanchen Wu; Jiayuan Ding; Subhabrata Mukherjee; Carl Yang; | arxiv-cs.AI | 2026-08-14 |
| 164 | Better Decomposition, Free Aggregation: A Synthesizer-Folding Framework for Multilingual Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We address both with our method Syfer, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default. |
YILIN WANG et. al. | arxiv-cs.CL | 2026-08-13 |
| 165 | Polish Medical Visual Question Answering: Vision-Language Models Underutilize Visual Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a Polish-language medical visual question answering (VQA) benchmark, built from Polish Board Certification Examination questions for licensed physicians and dentists pursuing specialist certification. |
JAKUB POKRYWKA et. al. | arxiv-cs.AI | 2026-08-13 |
| 166 | Asymptotic Risk Calibration for Selective Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, heuristic uncertainty scores cannot perfectly distinguish correct predictions from incorrect ones, and directly applying a fixed uncertainty threshold provides no statistical control over the error rate among accepted answers. To address this limitation, we propose A-CRC-QA, a post-hoc calibration framework for uncertainty-aware selective question answering. |
Shufan Lin; Sijin Dong; | arxiv-cs.CL | 2026-08-12 |
| 167 | Machine Unlearning for Speech Question Answering in Large Audio-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study machine unlearning for speech QA in LALMs, a setting that is more challenging than prior work on text-based Large Language Models (LLMs) or Automatic Speech Recognition (ASR) due to the tight coupling between acoustic perception and factual knowledge. |
Zhe Liu; | arxiv-cs.LG | 2026-08-12 |
| 168 | CapProbe: Evaluating Detailed Image Captions Via Full-Scene Dense Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CapProbe, a full-scene dense QA benchmark that turns detailed caption evaluation into region-aligned factual checking. |
Mouxiao Huang; Qiangyu Yan; Borui Jiang; Han Shu; | arxiv-cs.CV | 2026-08-11 |
| 169 | ENTLORE: A Graph-Grounded Benchmark for Latent Organizational Reasoning in Enterprise Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ENTLORE, a graph-grounded benchmark construction framework that reconstructs an audited enterprise world from routine documents, authoritative organizational tables, and operational records. |
Akrin Zheng; Alexander Wu; Alaia Liu; | arxiv-cs.IR | 2026-08-11 |
| 170 | Qwen-MusicAVQA-7B: A Multimodal Model for Music Audio-Visual QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We show that a lightweight alternative can be highly effective for music audio-visual question answering (AVQA). |
Maryam Dehdashti; | arxiv-cs.SD | 2026-08-11 |
| 171 | R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing long-video QA methods mainly emphasize temporal grounding and clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes. |
KE MA et. al. | arxiv-cs.CV | 2026-08-11 |
| 172 | A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Abstract Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis. To address this, we present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making. |
Syed Nazmus Sakib; Nafiul Haque; Mohammad Zabed Hossain; Shifat E. Arman; | Scientific Data | 2026-08-10 |
| 173 | How Robust Are LLMs to Vietnamese Dialects? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce VialectBench (Vietnamese Dialects Benchmarking), a controlled benchmark for testing whether model decisions remain stable across six Vietnamese dialect groups. |
MINH TRAN et. al. | arxiv-cs.CL | 2026-08-10 |
| 174 | From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a multi-modality medical equipment maintenance question-answering (QA) framework and demonstrate the fine-tuning of a medical foundation model for specialized technical troubleshooting tasks. |
BERNES LORIER ATABONFACK et. al. | arxiv-cs.AI | 2026-08-09 |
| 175 | Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from An IR Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we argue that the core ideas underlying RAG are not new: foundational concepts such as integrating retrieval and language generation, knowledge augmentation, answer verification, and iterative query (or prompt) refinement had already been studied and instantiated in information retrieval (IR) and question answering (QA) research dating back to the early 2000s, well before the emergence of LLMs. |
Xiaoyan Zhao; Yujie Cai; Yang Zhang; Grace Hui Yang; Tat-Seng Chua; | arxiv-cs.AI | 2026-08-08 |
| 176 | VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning Over Hierarchical Enterprise Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing RAG approaches largely overlook the holistic integration of diverse retrieval strengths, leading to inaccurate domain routing, poor utilization of hierarchical document structures, and consequently limited reasoning capabilities over enterprise knowledge. To address these limitations, we present VDGR-RAG, which integrates vector retrieval, directory-driven reasoning, graph traversal, and iterative reflection in a unified framework for accurate enterprise knowledge QA. |
Wenqi Chen; Haofei Yang; Rui Yang; Fangming Li; | arxiv-cs.AI | 2026-08-08 |
| 177 | FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval Over SEC Filings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: FinRank provides an evidence-first benchmark for developing financial question answering systems that are not only accurate but also grounded in the correct disclosure. |
Sasan Mansouri; Daniel Saad; Mark Wahrenburg; Manu Weissel; Fabian Woebbeking; | arxiv-cs.AI | 2026-08-07 |
| 178 | LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present LitTraceQA, a benchmark for literature-grounded question answering over scientific papers. |
XUYE LIU et. al. | arxiv-cs.CL | 2026-08-07 |
| 179 | Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context. To address this gap, we propose CondMedQA, the first benchmark for conditional biomedical QA, consisting of multi-hop questions whose answers vary with patient conditions. |
JASH RAJESH PAREKH et. al. | kdd | 2026-08-06 |
| 180 | From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose EviPath, an evidence-anchored reasoning path synthesis paradigm for RAG agent development. |
MUZHI LI et. al. | kdd | 2026-08-06 |
| 181 | Retain to Refine: Adaptive Online Question Answering Via Query Routing and Long-Short Memory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These include: (1) handling both simple and complex queries with appropriate levels of reasoning, (2) minimizing latency without compromising answer quality, and (3) maintaining answer consistency under evolving and noisy retrieval contexts. To address these challenges, we propose Retain-to-Refine (ℜ2ℜ), an adaptive agent-based QA framework designed for practical deployment. |
YUCHEN LI et. al. | kdd | 2026-08-06 |
| 182 | BioFlowBench: A Comprehensive Benchmark for Evaluating Bioinformatics Tool-use Capabilities of LLMs and Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose BioFlowBench, a comprehensive benchmark designed to shift from static knowledge assessment to dynamic execution evaluation in bioinformatics tool utilization. |
YUFEI HOU et. al. | kdd | 2026-08-06 |
| 183 | MCoRe: Multi-Entry Complementary Retrieval with Reflection-Guided Iteration for Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA. |
JUXIANG ZENG et. al. | kdd | 2026-08-06 |
| 184 | SciChart: Visual Question Answering and Reasoning for Scientific Spectral Chart Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing chart question answering (QA) datasets are monolingual with simple questions, making current evaluation benchmarks inadequate for the rapid advancements in MLLM performance. Therefore, we propose a multilingual scientific spectral Chart QA dataset, termed SciChart. |
TAN YUE et. al. | kdd | 2026-08-06 |
| 185 | Fighting Numerical Hallucinations Via Data-centric Compilation for Online Financial QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we pioneer a data-centric paradigm and propose a novel framework, the Data-centric Reasoning Compiler (DCRC). |
HAO CHEN et. al. | kdd | 2026-08-06 |
| 186 | UniDocVLM: Enhancing Visual Reasoning and Document Understanding for VLM Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose UniDocVLM, an end-to-end framework that unifies OCR and visual RAG within a single vision-language model: the model first generates a structured parse of retrieved pages, then activates question-relevant evidence from the parse to support grounded reasoning and answering. |
ZONGSHENG CAO et. al. | kdd | 2026-08-06 |
| 187 | Diagnosing Evidence Utilization in Multimodal Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we conduct a controlled empirical study of 7 popular MLLMs on long multimodal multi-document question answering in a RAG setting. |
Debolena Basak; Digbalay Bose; Koustava Goswami; Maunendra Sankar Desarkar; | kdd | 2026-08-06 |
| 188 | VISPATH: Visual-Intent-Guided Path Reasoning for Multimodal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a result, they cannot exploit multimodal cues that become important at intermediate hops. To address this limitation, we propose VISPATH, a visual-intent-guided path reasoning framework for MM-KGQA. |
Jinke Wu; Zhengpin Li; Mengzhe Jia; Yang Li; Wentao Zhang; | arxiv-cs.CL | 2026-08-04 |
| 189 | Diversity Is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: They also process queries uniformly, resulting in wasteful computation. We propose ARCHIVE (Ambiguity Recognition via Cascaded Hypothesis Inspection and Conflict Verification), an accurate and efficient framework that detects ambiguity via logical conflict: a query is ambiguous when its valid answers cannot all be true under a single interpretation. |
Jiwon Lee; Yong-chan Park; Jungin Hong; U Kang; | arxiv-cs.AI | 2026-08-04 |
| 190 | Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Bayesian Data Reweighting, a probabilistic framework that models query-document importance as latent variables and adaptively infers posterior weights to downweight likely false negatives. |
JINGCHEN SUN et. al. | arxiv-cs.LG | 2026-08-03 |
| 191 | MITRE-SAGE: A Multi-Agent Cybersecurity Question-Answering Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work proposes MITRE-SAGE, a multi-agent retrieval-augmented generation framework that integrates semantic and structural cybersecurity knowledge to improve the reliability and interpretability of LLM-based QA systems. |
Ali Habibzadeh; Farid Feyzi; Reza Ebrahimi Atani; | arxiv-cs.IR | 2026-08-03 |
| 192 | Deep Agentic Search for Repository-Level Code Question Answering: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We compare the two approaches on SWE-QA, a benchmark for repository-level code question answering. |
AMIRKIA RAFIEI OSKOOEI et. al. | arxiv-cs.SE | 2026-08-02 |
| 193 | DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our key insight is that agents should navigate document structure within and across documents rather than repeatedly search from scratch. |
DONGYANG XIE et. al. | arxiv-cs.CL | 2026-08-02 |
| 194 | Ground, Cover, and Refine: Evidence-Centric Frame Selection for Long-Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose GCR, a training-free framework that casts fixed-budget frame selection as a joint evidence curation problem. |
FAN WEI et. al. | arxiv-cs.CV | 2026-08-02 |
| 195 | Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Reinforcement learning (RL) has shown promise in improving LLMs’ search capabilities, but it often suffers from sparse rewards during training, hindering the model’s ability to learn effectively. To address these challenges, we introduce Guided Retrieval Training (GRT), a novel method that improves the performance of a search agent by restricting the retrieval process during RL training using ground truth information. |
Aounon Kumar; Sudipta Paul; Vivek Kulkarni; Vijay Srinivasan; Srinivas Chappidi; | arxiv-cs.AI | 2026-08-01 |
| 196 | Exploiting Intrinsic Duality for Multi-Hop Question Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although MQG and QA are inherently dual tasks, most existing MQG studies largely overlook this intrinsic duality. To address this limitation, we propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation. |
Maodong Li; Xinyue Kang; Yuanchen Shi; Fang Kong; | arxiv-cs.CL | 2026-08-01 |
| 197 | Zero-Mem: Zero-Token Memory Operations for LLM Agents Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating … |
YILIN XIAO et. al. | arxiv-cs.CL | 2026-07-31 |
| 198 | SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose SeDeM, a selective decompression framework that decouples compact memory storage from decoder conditioning. |
Maryam Haghifam; Jason Cong; Yizhou Sun; | arxiv-cs.CL | 2026-07-31 |
| 199 | AI-Based Document Analysis and Question Answering System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In order to extract valuable insights from documents, this study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches. |
Radhika Sharma; Devraj Gautam; | Revolutionary Advances in Computing and Electronics: An … | 2026-07-31 |
| 200 | ViewMind3D: Modular View-Aware Inference for Training-Free 3D-QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present \textbf{ViewMind3D}, a fully training-free and modular framework for 3D spatial reasoning over multi-view observations of a scene without requiring complete 3D reconstruction. |
PING-KUN CHIANG et. al. | arxiv-cs.CV | 2026-07-30 |
| 201 | MedLLM: An Open Medical Language Model at The Sub-Billion Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present MedLLM, an open 0.1B-parameter medical language model trained through a fully open three-phase pipeline: general pretraining with curriculum sequence-length scheduling, domain fine-tuning on MedFineWeb, a reference-guided medical corpus we release that is selected from general web data by embedding similarity to medical question-answering (QA) data, and preference-aligned fine-tuning combining SFT with direct preference optimization (DPO). |
Maxx Richard Rahman; Asim Ahmed; Mihan Mohagheghzadeh; Wolfgang Maass; | arxiv-cs.AI | 2026-07-29 |
| 202 | GraphQAG: A Knowledge-Graph-Guided Visual Analytics Framework for Question-Answer Pairs Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present GraphQAG, a knowledge graph-guided visual analytics framework for generating high-quality QA pairs from long documents. |
YIZE LI et. al. | arxiv-cs.HC | 2026-07-29 |
| 203 | Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing. |
Chandan Kumar Sah; Xiaoli Lian; Li Zhang; | arxiv-cs.IR | 2026-07-28 |
| 204 | A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering Over Irregular Clinical Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent multimodal time-series large language models (LLMs) have shown considerable promise in general-purpose time-series QA, they remain poorly equipped to model the sparsity, asynchrony, and irregular sampling patterns of clinical observations. To fill this gap, we propose ClinPRISM, a cost-effective multimodal LLM reasoning framework for question answering over ICTS data. |
Frank Nie; Ethan B Liu; Yuan Zhu; Wei Fan; Jindong Han; | arxiv-cs.AI | 2026-07-28 |
| 205 | Visual Question Answering Model Based on Multi-modal Knowledge Autonomous Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yuanlong Wang; Zhiru Xu; Houshuai Wang; Zhiwei Wu; Hu Zhang; | Multimedia Tools and Applications | 2026-07-28 |
| 206 | Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SKIP (Salient Knowledge-Injected Pathways), a unified inference architecture that routes computation along sparse pathways jointly conditioned on the question, the image, and a difficulty estimate. |
Noor Islam S. Mohammad; Uluğ Bayazıt; | arxiv-cs.AI | 2026-07-28 |
| 207 | Mgsc: Multimodal Generation and Self-supervised Contrast Learning for Mitigating Language Bias in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
XINYU JIANG et. al. | Multimedia Systems | 2026-07-27 |
| 208 | HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods often suffer from unreliable cross-modal evidence indexing and expensive graph traversal. To address these issues, we propose HVM-GraphRAG, a holistic-view multimodal GraphRAG framework on complex document. |
XIN HE et. al. | arxiv-cs.IR | 2026-07-26 |
| 209 | DDVT: Dynamic Dual-level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce the Dynamic Dual-level Vision Transformer Fusion Network (DDVT) for answer grounding in visual question answering. |
Yue Zhang; Xiangyu Li; Wanshu Fan; Xin Yang; Dongsheng Zhou; | arxiv-cs.CV | 2026-07-26 |
| 210 | Touching or Chatting: The Utility of LLMs and Tactile Charts for Learning About Complex Chart Types By BLV Individuals Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate how LLMs influence chart-type learning and whether tactile learning improves subsequent LLM-supported exploration. |
Tingying He; Maggie McCracken; Daniel Hajas; Sarah Creem-Regehr; Alexander Lex; | arxiv-cs.HC | 2026-07-25 |
| 211 | Co-Evolving Graph and Text Memory for Training-Free Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Co-E, a training-free system built around synchronized bidirectional graph-text working memory. |
Hieu Man; Thien Huu Nguyen; | arxiv-cs.CL | 2026-07-25 |
| 212 | Difference Visual Question Answering in Medical Imaging Based on Multimodal Large Models and Enhanced By Prior Region-level Difference Descriptions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, pixel-level matching is unreliable due to non-rigid deformations, view shifts, and acquisition noise, while existing models often rely on synthetic labels and lack effective integration of local and global information. To address these challenges, we propose a multimodal large-model framework that adopts a progressive “local semantic modeling–global difference reasoning” strategy. |
BOKAI YANG et. al. | Information Technology and Control | 2026-07-23 |
| 213 | Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English question with financial statements and … |
ZHUOHAN XIE et. al. | arxiv-cs.CL | 2026-07-22 |
| 214 | TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present TAP-RAG, a task-aware policy-controlled RAG framework for long-document multimodal QA. |
Zhong Ji; Keqi Jin; Yan Zhang; Jiasheng Li; | arxiv-cs.CV | 2026-07-21 |
| 215 | D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents D3VL, a novel MLLM framework that integrates 2D and 3D time-series data in a single but simple architecture. |
Heesang Han; A. Lynn Abbott; Abhijit Sarkar; | arxiv-cs.CV | 2026-07-21 |
| 216 | UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. |
Mohammad Arvan; Hossein Haeri; Natalie Parde; Rebecca T. Feinstein; | arxiv-cs.CL | 2026-07-21 |
| 217 | Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present FiT (Find before Fine-Tune), a task-oriented diagnostic framework that characterizes small LLMs along three capabilities required for cybersecurity QA: vocabulary recognition, parametric knowledge, and contextualization of retrieved information. |
SHASWATA MITRA et. al. | arxiv-cs.CL | 2026-07-21 |
| 218 | Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While blind or low-vision (BLV) scientists have traditionally relied on static alternative text to access figures in papers, the rise of artificial intelligence (AI) has made interactive question-answering (QA) a feasible paradigm for visual exploration; yet little is known about how scientists use visual QA in practice or how to improve its accessibility. In this work, we interview five BLV and five sighted scientists across different STEM fields to understand how they use two AI tools, ChatGPT and Gemini, to query multimodal scientific documents. |
Arnavi Chheda-Kothary; Lucy Lu Wang; Joseph Chee Chang; Jonathan Bragg; | arxiv-cs.HC | 2026-07-20 |
| 219 | Enhancing Semantic Question Similarity in Arabic Using Hybrid Models and Knowledge Graph Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing semantic similarity approaches have inherent trade-offs: KGs capture deep meaning but lack adaptability; corpus -based methods, such as word embeddings, rely on statistical patterns without deep semantic understanding; and deep learning models efficiently perform but lack interpretability. In addressing these limitations, this study proposes a hybrid SQS model that integrates knowledge graph embedding (KGE) with AraBERT embeddings to enrich semantic representation. |
Ammar Al-Edhari; Mohsen Kahani; | PeerJ Computer Science | 2026-07-20 |
| 220 | FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FinSAgent, an evidence-grounded multi-agent framework that reframes SEC filing QA as corpus-aligned retrieval planning and corrects both ends with a single principle: inject corpus-side conditioning wherever model priors would otherwise dominate. |
JIJUN CHI et. al. | arxiv-cs.IR | 2026-07-20 |
| 221 | SEGRA: Structured Experience-Guided Graph Reasoning Agent for Gremlin Based Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SEGRA, an experience-guided agent for enterprise text-to-Gremlin question answering. |
SAIYUE LYU et. al. | arxiv-cs.AI | 2026-07-20 |
| 222 | Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Debate-on-Graph (DoG), a new framework that enables LLMs and UKGs to collaborate adaptively for reliable reasoning. |
Peiji Yu; Xin Chen; Tianxing Wu; | arxiv-cs.CL | 2026-07-19 |
| 223 | Looking Beyond Visible Cues: Implicit Video Question Answering Via Dual-Clue Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save |
TIEYUAN CHEN et. al. | International Journal of Computer Vision | 2026-07-19 |
| 224 | Context Convergence Improves Answering Inferential Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) are widely used in open-domain Question Answering (QA), their ability to handle inferential questions—where answers must be derived rather than directly retrieved—remains still underexplored. This study investigates how the structure and quality of passages influence LLM performance on such questions. |
Jamshid Mozafari; Bhawna Piryani; Adam Jatowt; | sigir | 2026-07-17 |
| 225 | An Unsupervised Graph Attention Network Empowered By Large Language Models for Knowledge Graph Grounded Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Trung Vo; Son T. Luu; Le-Minh Nguyen; | Computers and Electrical Engineering | 2026-07-17 |
| 226 | Corpus-Centric Learning for Zero-Shot Table Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose GeCo-TR (Generative Schema and Contrastive Table Retrieval), a zero-shot table retrieval framework that eliminates the need for supervised QA data by shifting from direct query-to-table learning to modeling the intrinsic structural semantics of the table corpus. |
Zhou He; Zhifei Pang; Xiu Tang; Sai Wu; Gang Chen; | sigir | 2026-07-17 |
| 227 | Code-Based English Models Reveal Surprising Performance on Chinese QA Pair Extraction Task Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper explores advancements in automated Question-Answer (QA) extraction using large language models (LLMs), addressing challenges in transforming unstructured text into high-quality, retrievable QA pairs. |
JIAJUN YU et. al. | sigir | 2026-07-17 |
| 228 | IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This gap is critical because without understanding which intents users prioritize, systems cannot generate responses satisfying individual information needs. To address this, we introduce the concept of core intents: intents users prioritize when selecting answers to satisfy their information needs. |
Jieyong Kim; Maryam Amirizaniani; Soojin Yoon; Dongha Lee; | sigir | 2026-07-17 |
| 229 | Mitigating Bias in Large Language Model Based Question Answering Through Causal Front Door Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Causal Front Door Prompting framework (CFDP) that reduces demographic influence by intervening on the chain of thought reasoning, which is treated as an observable mediator. |
YAQI YANG et. al. | sigir | 2026-07-17 |
| 230 | Searching Videos As Trees: Self-Correcting Agents for Grounded Long Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose VideoTreeSearch (VTS), a framework that casts grounded LVQA as iterative self-correcting search over an adaptive temporal tree. |
CE ZHANG et. al. | arxiv-cs.CV | 2026-07-17 |
| 231 | Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose PR2 (Personalized Retrieval-Augmented Reasoning), a reinforcement learning framework that integrates reasoning and retrieval from personal context for personalization. |
Maryam Amirizaniani; Alireza Salemi; Hamed Zamani; | sigir | 2026-07-17 |
| 232 | DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Deep-research agents are capable of executing multi-step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, … |
TONGZHOU WU et. al. | sigir | 2026-07-17 |
| 233 | Learning from Natural Language Feedback for Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce VAC, a novel framework for personalized response generation that replaces scalar rewards with natural language feedback (NLF) that are generated conditioned on the user profiles and the question narratives. |
Alireza Salemi; Hamed Zamani; | sigir | 2026-07-17 |
| 234 | RAPO: Reason-Aware Preference Optimization for Factual Table Question Answering Over Structured Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce Reason-Aware Preference Optimization (RAPO), a novel alignment framework that incorporates reason-augmented preference signals to better guide LLM learning and improve the reliability of structured information retrieval. |
Phaniram Sayapaneni; Musen Wen; Sunil Goda; | sigir | 2026-07-17 |
| 235 | How Fine-Grained Should A RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present HieraRAG, a hierarchical framework for studying granularity in RAG benchmark construction, defining optimal granularity as the level that maximizes discriminative power (the standard deviation of generation quality across categories) within a given RAG configuration. |
Chase M. Fensore; Kaustubh Dhole; Jason Fan; Eugene Agichtein; Joyce C. Ho; | sigir | 2026-07-17 |
| 236 | Toward A Telugu Question Answering Dataset for Agricultural IR to Serve Marginal Farmers in Drought-Prone Rayalaseema: A Gap Analysis and Roadmap Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We examine the current landscape of agricultural information retrieval resources for Telugu-speaking farmers and find a critical gap: while agricultural QA datasets exist for Hindi, Tamil, and Bengali, no Telugu agricultural QA benchmark exists, let alone one addressing drought-specific contexts. We document available information sources, identify where they fall short, and propose a methodology for constructing a Telugu Agricultural QA dataset using Kisan Call Center records and regional extension materials. |
Piyush Joshi; Priyansh Singhal; | sigir | 2026-07-17 |
| 237 | SSR: Structured Subgraph Retrieval for Temporal Knowledge Graph Question Answering with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, selecting top-n facts based on semantic similarity inevitably introduces a large amount of irrelevant noise into the LLM input, which degrades reasoning performance. To address these limitations, we propose SSR, a Structured Subgraph Retrieval framework for TKGQA with LLMs. |
Ying Zhang; Li Zhang; Wenya Guo; Shilong Ping; Xinying Qian; | sigir | 2026-07-17 |
| 238 | Automating Generation of Long-Form Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional short keyword queries are increasingly being replaced by longer, more detailed queries that reflect complex and nuanced user information needs, especially in conversational assistants equipped with web search capabilities. In this work, we present a methodology for automatically generating such human-style long-form queries (narratives) by clustering raw short queries to form synthetic search sessions, designed to reflect a real user’s search behavior. |
SHIVANI UPADHYAY et. al. | sigir | 2026-07-17 |
| 239 | Exploring and Improving Cross- and Multi-Domain Personalized Question Answering Via A Dual Retrieval Augmentation Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we use the LaMP-QA benchmark, where user profiles span diverse Stack Exchange domains (Arts & Entertainment, Lifestyle & Personal Development, and Society & Culture), to systematically investigate how domain composition of the user profile affects personalization performance. |
Ozel Yilmazel; Hamed Zamani; James Allan; | sigir | 2026-07-17 |
| 240 | Agnostic Multi-Source Retrieval-Augmented Generation for Documents and Database Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study develops a multi-source Retrieval-Augmented Generation (RAG) based Question Answering (QA) system that automatically integrates heterogeneous knowledge sources through a unified source parameter. |
Krisna Dwi Setya Adi; Ivan Michael Siregar; | Jurnal Ragam Pengabdian | 2026-07-17 |
| 241 | Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering Via Reasoning-Free Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Perception-RFT, a training framework that applies Group Relative Policy Optimization (GRPO) to multimodal document QA, bypassing intermediate reasoning tokens to directly align visual features with structured grounding outputs. |
HARIKRISHNAN P M et. al. | arxiv-cs.AI | 2026-07-16 |
| 242 | Bridging Legal Language Barriers Using Explainable AI: Outcome Prediction and Multilingual Knowledge Based Answer Retrieval for Indian Law Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study presents an integrated legal AI platform that combines interpretable case outcome prediction with multilingual, retrieval-grounded legal question answering to improve access to Indian law. |
Manish Thirunavu D; | International Journal of Latest Technology in Engineering … | 2026-07-16 |
| 243 | Expanding The Lexicon of Ge’ez Based African Languages: A Comparative Study of Amharic and Tigrinya Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our contributions are: (i) a vocabulary-extension and embedding-initialization procedure tailored to Ge’ez script; (ii) a two-stage training strategy under which vocabulary and continued-pretraining gains on Amharic/Tigrinya transfer to 17 typologically related, unaugmented African languages; and (iii) an evaluation spanning both intrinsic tokenization metrics (vocabulary coverage, fertility, OOV rate) and extrinsic task performance across all 19 languages. |
Hailay Kidu Teklehaymanot; Debela Desalegn Yadeta; Wolfgang Nejdl; | arxiv-cs.CL | 2026-07-16 |
| 244 | CMI-Mem: Toward Generalizable Long-Term Memory Management Via CMI-Augmented Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods rely predominantly on LLM-judged synthetic question-answer (QA) pairs, making memory valuation dependent on sampled queries and the downstream reader. To address this limitation, we propose \textbf{CMI-Mem}, a reinforcement learning(RL)-based lightweight memory manager model with a hybrid reward that combines downstream QA correctness and intrinsic Conditional Mutual Information (CMI). |
YUBO WANG et. al. | arxiv-cs.AI | 2026-07-15 |
| 245 | EgoProceVQA: A Novel Egocentric Procedural Understanding Task with Self-Skill-Exploration Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such capabilities are crucial for building procedural AI assistants deployable on wearable devices. To bridge this gap, we introduce the Egocentric Procedural Understanding VQA task (EgoProceVQA), which systematically evaluates egocentric procedural reasoning abilities of current MLLMs and agents through six types of key-step-centric questions. |
JUNLONG LI et. al. | arxiv-cs.CV | 2026-07-15 |
| 246 | Automatic Question Answering Systems: A Comprehensive Review Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The Question Answering system combines research from several fields, including Natural Language Processing, Artificial Intelligence, Information Retrieval, and Information Extraction. |
Sanah Nashir Sayyed; Bharat Shelke; C. Namrata Mahender; | International Journal for Research in Applied Science and … | 2026-07-14 |
| 247 | Technical Report on The CVPR 2026@AdvML Workshop Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This report presents the CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs. |
TIANYUAN ZHANG et. al. | arxiv-cs.CV | 2026-07-13 |
| 248 | Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We ask how reliably such confidently wrong answers, or confident hallucinations, can be detected from a model’s internal activations, and whether those activations carry information beyond its observable outputs. We train linear probes on the residual stream and evaluate them on two established question-answering (QA) benchmarks built from real filings, FinQA and TAT-QA. |
Richard Zhe Wang; | arxiv-cs.CL | 2026-07-13 |
| 249 | LakeQuest: A Three-Domain Benchmark for Grounded Question Answering Across Data Lakes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current benchmarks abstract away this noisy discovery process, failing to evaluate end-to-end performance. To bridge this gap, we introduce LakeQuest, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes. |
MICHAEL SOLODKO et. al. | arxiv-cs.CL | 2026-07-13 |
| 250 | DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, such errors are often recalcitrant to tracing and calibration, posing a critical bottleneck for their practical deployment in geospatial tasks. To address this pressing challenge, this study proposes DM-KG (Direction-Metric Knowledge Graph), a structurally grounded spatial representation framework for street view imagery. |
XINYUE XU et. al. | arxiv-cs.CV | 2026-07-13 |
| 251 | IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose IterCOMP, a unified, training-free prompt compression framework that incorporates multi-hop reasoning within an iterative compression loop. |
JungMin Yun; YoungBin Kim; | arxiv-cs.CL | 2026-07-12 |
| 252 | CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CRiT-QA (Counterfactual Reasoning with Traps), a dataset explicitly designed to address both limitations. |
JungMin Yun; JuneHyoung Kwon; YoungBin Kim; | arxiv-cs.AI | 2026-07-12 |
| 253 | STEC: Evidence Compression for Deep Search in Open-domain Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Directly comparing raw trajectories exposes the verifier to noisy and unaligned content, while comparing answer strings ignores the evidence supporting each candidate, making reliable final selection difficult. To address this challenge, we propose STEC, an evidence compression framework for final answer selection in multi-hop QA. |
XINKANG LI et. al. | arxiv-cs.AI | 2026-07-12 |
| 254 | CLIR-Bench: Benchmarking Multimodal Question Answering Over Irregular Clinical Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmarks primarily focus on regularly sampled time-series QA or medical QA over static data, and therefore rarely assess whether models can faithfully ground their answers in irregular temporal observations. To fill this gap, we introduce CLIR-Bench, a benchmark for irregular clinical time series QA constructed from de-identified ICU records through a principled four-stage pipeline. |
FRANK NIE et. al. | arxiv-cs.CL | 2026-07-10 |
| 255 | Task-Specific Multimodal Question Answering Agents Via Confidence Calibration and Incremental Reasoning for QANTA 2026 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). |
Nirjhar Das; Md. Al-Mamun Provath; | arxiv-cs.CL | 2026-07-10 |
| 256 | Interpretable Uncertainty for Adaptive Retrieval and Reasoning in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose an uncertainty-aware framework for adaptive QA based on explicit signals derived from LLM internal representations. |
Ritajit Dey; Iadh Ounis; Graham McDonald; | arxiv-cs.IR | 2026-07-08 |
| 257 | Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To assess realistic free-form answering, we introduce a rubric-based LLM-as-a-judge covering faithfulness, completeness, clarity, and factual consistency, and validate it against dual human annotations. |
FELIX FELDMAN et. al. | arxiv-cs.CL | 2026-07-07 |
| 258 | Mitigating Factual Hallucination in Large Reasoning Models Via Mixed-Mode Advantage Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To explain this phenomenon, we formulate explicit thinking in factuality QA as a thinking residual over the model’s direct-answer tendency, which can either recover missing knowledge or introduce unsupported associations. Based on this formulation, we propose MARGO, \underline{\textit{M}}ixed-Mode \underline{\textit{A}}dvantage \underline{\textit{R}}egularization for \underline{\textit{G}}rounded \underline{\textit{O}}ptimization, a reinforcement learning framework that uses non-thinking rollouts as same-model references in advantage estimation. |
KAISHEN WANG et. al. | arxiv-cs.CL | 2026-07-07 |
| 259 | From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study presents a question-type-specific large language model (LLM) framework for BioASQ 14b Task B, designed to improve answer robustness and evidence grounding in biomedical question answering. |
TAEYUN ROH et. al. | arxiv-cs.CL | 2026-07-07 |
| 260 | Retrieving A Set, Not Independent Passages: Set-Level Compatibility Learning for Efficient Set Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: LLM-based set selection can model such interactions, but its computational cost limits practical use. We address this gap by formulating multi-hop retrieval as query-set compatibility scoring and propose a set-level retrieval framework. |
Mooho Song; Jay-Yoon Lee; | arxiv-cs.IR | 2026-07-06 |
| 261 | Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CIC, a confidence-interval-based calibration framework that converts arbitrary uncertainty scores into risk-controlled selective answering rules. |
Sijin Dong; Hiroyuki Shinnou; | arxiv-cs.CL | 2026-07-05 |
| 262 | Seeing Once Is Enough? Online Geometry-Aware Token Pruning for 3D Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To mitigate this issue, existing solutions rely on strategic frame selection or token-merging algorithms that require preprocessing in advance all frames of the scene, i.e., an offline fashion. In contrast, we propose the first online token-pruning method that can be integrated seamlessly with current MLLM models for 3D question answering tasks, without additional training and with lower memory usage.Our key insight is to project each input frame into a shared voxel space using depth information and camera pose, identifying spatially-overlapped regions across frames and selectively pruning redundant image tokens before they enter the language model. |
Ruei-Chi Lai; Bolivar Solarte; Chin-Hsuan Wu; Yi-Hsuan Tsai; Min Sun; | arxiv-cs.CV | 2026-07-04 |
| 263 | HETERQA: Benchmarking Record Retrieval Over Multiple Heterogeneous Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing benchmarks are constructed from individual sources, and only a very few recent benchmarks have considered two or three sources. To alleviate this issue, we introduce HETERQA, a comprehensive benchmark with 857 QA pairs for record retrieval over five heterogeneous sources. |
Yaodong Su; Hanchang Li; Quanqing Xu; Chuanhui Yang; Yixiang Fang; | arxiv-cs.IR | 2026-07-03 |
| 264 | IDEAL-Bench: Indoor Dataset and Evaluation Suite for Analyzing 3D Layout Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we introduce IDEAL-Bench, an evaluation suite that requires VLMs to predict structured 3D layouts on photorealistic indoor scenes across 10 room types, scored along five numerical dimensions and a perceptual render-and-compare protocol. |
Yuening Cai; Junwei Zhou; Youran Qu; Yu-Wing Tai; | arxiv-cs.CV | 2026-07-03 |
| 265 | Transformers and BiLSTM-Based Ensemble Modeling for Entity and Relation Aware Biomedical Extractive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study introduces a novel transformer-based approach for biomedical EQA that incorporates Named Entity Recognition (NER) to identify key medical terms, Relation Extraction (RE) to understand their interconnections, and a BiLSTM layer to enhance contextual comprehension. |
Ahmed Ajmine Nehal; Md Mehedi Hasan; Farhana Elias; Rashedur M. Rahman; | Vietnam Journal of Computer Science | 2026-07-03 |
| 266 | ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ReQuest , an uncertainty-driven, question-adaptive keyframe selection pipeline that aligns question intent with relevant video content through selective computation. |
MINKUK KIM et. al. | arxiv-cs.CV | 2026-07-02 |
| 267 | LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper investigates whether text-to-speech (TTS) can provide task-specific training data for Luxembourgish SQA without requiring a large human-recorded QA corpus. |
Nina Hosseini-Kivanani; Marco Matassoni; Alessio Brutti; | arxiv-cs.CL | 2026-07-02 |
| 268 | MMTR: Strategy-Guided Multimodal Table Reasoning with Reflective Self-Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This limitation primarily stems from the high density of structured information inherent in tables and the scarcity of high-quality instruction tuning data. To address these challenges and improve the model’s reasoning accuracy in tables, we propose MMTR, a strategy-guided multimodal table reasoning method with reflective self-correction. |
Lixin Bai; Yibo Ming; Yanmin Chen; | Information | 2026-07-01 |
| 269 | Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In contrast, we study the complementary problem of intra-context conflict in multi-document RAG. To evaluate this setting, we introduce DRQA, a factual-conflict question answering benchmark derived from enterprise deep-research scenarios, where answers are grounded in synthetic enterprise-specific facts that are designed not to be recoverable from the model’s internal memory. |
RAYMOND LI et. al. | arxiv-cs.CL | 2026-07-01 |
| 270 | MAKES-QA: A Multi-agent Framework for Knowledge Graph Construction and Enrichment Over Scientific Literature for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Anna Borrelli; V. De Angelis; Stefano Izzo; F. Giampaolo; Francesco Piccialli; | Expert Syst. Appl. | 2026-07-01 |
| 271 | Imprint: Online Memory Compression for Long-Horizon Egocentric QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Imprint, an interaction-centric memory framework that formulates long-horizon egocentric memory as an online memory compression problem rather than summarization. |
Kousik Das; Debaditya Roy; | arxiv-cs.CV | 2026-07-01 |
| 272 | Multilingual Question Answering Over Linked Data Building on A Model of The Lexicon-Ontology Interface Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Multilingual question answering (QA) over linked data can make structured knowledge accessible across languages. However, most state-of-the-art QA systems are limited to English … |
Mohammad Fazleh Elahi; Basil Ell; Gennaro Nolano; Philipp Cimiano; | Semantic Web | 2026-07-01 |
| 273 | Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce an OpenMIC-derived diagnostic benchmark sequence for instrument grounding in music audio-language models, extending binary instrument-presence QA to genre-prior-reduced examples, confusable instrument discrimination, longer audio context, and temporal localization. |
Yujun Lee; Joonhyeok Shin; Hyoeun Kim; Kyuhong Shim; | arxiv-cs.SD | 2026-06-30 |
| 274 | Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Language models are increasingly taught from synthetic question–answer (QA) supervision: a model generates questions about a document, answers them from the same text, and the resulting pairs are used to fine-tune, distill, or compress knowledge into another model. |
EKATERINA ALIMASKINA et. al. | arxiv-cs.AI | 2026-06-30 |
| 275 | Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose BiRG-LoRA, a single-adapter rank-gated LoRA method for medical question answering. |
Yining Huang; | arxiv-cs.CL | 2026-06-30 |
| 276 | A Reciprocal Interaction Framework for Collaborative Temporal Grounding and Question Answering in Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a result, VQA models often generate ambiguous answers due to the lack of precise temporal cues, while VTG models fail to fully exploit the high-level semantic information embedded in the answers. To address these limitations, We propose a Reciprocal Interaction Framework (RIF). |
Jiaxu Wang; Tianshan Liu; Bing-Kun Bao; | ACM Transactions on Multimedia Computing, Communications, … | 2026-06-30 |
| 277 | JL1-CC&QA: Extending The JL1-CD Benchmark with Change Captioning and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why. To bridge this semantic gap, we introduce JL1-CC&QA, a multi-task benchmark that extends the JL1-CD dataset with two complementary annotation layers: change captioning (CC) and change question answering (QA). |
Ziyuan Liu; Ruifei Zhu; Ouqiao Ma; Yuantao Gu; | arxiv-cs.CV | 2026-06-30 |
| 278 | Pathways of Thoughts: Multi-Directional Thinking for Long-form Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Personalization is well studied in search and recommendation, but personalized question answering remains underexplored due to challenges in inferring preferences from long, noisy, implicit contexts and generating responses that are both accurate and aligned with user expectations. To address this, we propose Pathways of Thoughts (PoT), an inference-stage method that applies to any large language model (LLM) without task-specific fine-tuning. |
ALIREZA SALEMI et. al. | www | 2026-06-29 |
| 279 | Mind The Ambiguity: Aleatoric Uncertainty Quantification in LLMs for Safe Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The deployment of Large Language Models in Medical Question Answering is severely hampered by ambiguous user queries, a significant safety risk that demonstrably reduces answer accuracy in high-stakes healthcare settings. In this paper, we formalize this challenge by linking input ambiguity to aleatoric uncertainty (AU), which is the irreducible uncertainty arising from underspecified input. |
YAOKUN LIU et. al. | www | 2026-06-29 |
| 280 | Let It Try First: Uncertainty-Guided Retrieval Switching for Retrieval-Augmented Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches either follow fixed multi-stage pipelines or use a single-pass classifier to determine the strategy, leading to irreversible decisions once the strategy is selected. To address this limitation, we propose TF-RAG (Try First), an adaptive RAG framework for question-answering that first attempts to answer the question with the initial retrieval strategy, and can be dynamically switched to a stronger strategy if needed. |
Xiaohai Wang; Yu Liang; Wei Zhou; Sixing Wu; | www | 2026-06-29 |
| 281 | CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CompactRAG, a simple yet effective framework that decouples offline corpus restructuring from online reasoning. |
HAO YANG et. al. | www | 2026-06-29 |
| 282 | Inferential Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Inferential QA–a new task that challenges models to infer answers from answer-supporting passages which provide only clues. |
Jamshid Mozafari; Hamed Zamani; Guido Zuccon; Adam Jatowt; | www | 2026-06-29 |
| 283 | VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To overcome these challenges, we present VeritasFi, an innovative hybrid RAG framework that incorporates a multi-modal preprocessing pipeline alongside a cutting-edge two-stage training strategy for its re-ranking component. |
ZHENGHAN TAI et. al. | www | 2026-06-29 |
| 284 | Efficient Retrieval-Augmented Generation Via Token Co-occurrence Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent graph-based RAG methods improve the retrieval of interconnected chunks, they often rely on computationally expensive and error-prone LLM-based extraction pipelines. To address these issues, we propose TIGRAG (Token-Induced GraphRAG), an efficient graph-augmented RAG framework based on a token co-occurrence Knowledge Graph. |
GIANLUCA BONIFAZI et. al. | arxiv-cs.CL | 2026-06-29 |
| 285 | MMTableBench: A Multi-level Multimodal Benchmark for Reasoning and Layout Complexity in Table QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To advance TableQA beyond superficial task difficulty and toward interpretable capability modeling, we introduce MMTableBench, a multi-level benchmark that systematically evaluates MLLMs along two fine-grained dimensions: layout complexity and reasoning complexity. |
XIANJIE WU et. al. | www | 2026-06-29 |
| 286 | Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We further observe that query types show consistent regularities in their alignment with retrieval paths, suggesting that routing decisions can be effectively guided by systematic rules that capture these patterns. Building on these insights, we propose a rule-driven routing framework designed specifically for hybrid-source RAG. |
HAOYUE BAI et. al. | www | 2026-06-29 |
| 287 | Data-Driven Function Calling Improvements in Large Language Model for Online Financial QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a data-driven pipeline to enhance function calling in LLM for our online, deployed financial QA, comprising dataset construction, data augmentation, and model training. |
XING TANG et. al. | www | 2026-06-29 |
| 288 | From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Both limitations can misguide reasoning and compromise answer reliability. To overcome these challenges, we propose MedRGAG, a unified retrieval–generation augmented framework that seamlessly integrates external and parametric knowledge for medical QA. |
Lei Li; Xiao Zhou; Yingying Zhang; Xian Wu; | www | 2026-06-29 |
| 289 | How Far Can You Get Without A GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we explore a practical alternative: how well can hallucination detection perform using only lightweight, CPU-feasible methods built on publicly available models? |
Kriti Faujdar; Smit Kadvani; | arxiv-cs.CL | 2026-06-29 |
| 290 | ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we introduce ARMOR, Adaptive Regularized Mixture Optimization for Retrievers, which learns separate temperatures for the RAG retrieval distribution and InfoNCE softmax and regularizes the adapted query encoder toward the frozen base query encoder. |
Heshan Fernando; Quan Xiao; Yan Xin; Tianyi Chen; | arxiv-cs.IR | 2026-06-28 |
| 291 | Beyond Scaling: Measuring and Predicting The Upper Bound of Knowledge Retention in Language Model Pre-Training Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Size-dependent Mutual Information (SMI), an information-theoretic predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering (QA) accuracy. |
CHANGHAO JIANG et. al. | acl | 2026-06-27 |
| 292 | AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These difficulties are primarily due to hallucination and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding. |
TAOLIN ZHANG et. al. | acl | 2026-06-27 |
| 293 | Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a discourse-aware hierarchical framework that leverages rhetorical structure theory (RST) for long document question answering. |
HUIYAO CHEN et. al. | acl | 2026-06-27 |
| 294 | Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for multi-hop, domain-specific evidence selection; and (2) evidence-constrained reinforcement learning via Group Relative Policy Optimization (GRPO) with multi-dimensional rewards covering faithfulness, style compliance, safety, and URL validity. |
WENWEI LI et. al. | acl | 2026-06-27 |
| 295 | HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing approaches relying on flat text chunks or page-level images inherently struggle to (i) precisely pinpoint the target document among thousands of candidates and (ii) organically connect multimodal evidence, such as tables and figures, within a limited token budget. To address these challenges, we propose HiKEY, a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal. |
Joongmin Shin; Gyuho Shim; Jeongbae Park; Jaehyung Seo; Heuiseok Lim; | acl | 2026-06-27 |
| 296 | EVE: A Domain-Specific LLM Framework for Earth Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. |
ÀLEX R. ATRIO et. al. | acl | 2026-06-27 |
| 297 | Protecting Multimodal Large Language Models Against Misleading Visualizations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We find that two methods, table-based QA and redrawing the visualization, are effective, with improvements of up to 19. |
Jonathan Tonglet; Tinne Tuytelaars; Marie-Francine Moens; Iryna Gurevych; | acl | 2026-06-27 |
| 298 | EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the challenges of real-world table QA, we propose **EASE**: **E**ntity-**A**ware **S**ub-table Generation for R**E**al-world Multi-table QA framework. |
MYUNGHOON KANG et. al. | acl | 2026-06-27 |
| 299 | CRAFT: Training-Free Cascaded Retrieval for Tabular QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work establishes a scalable and adaptable paradigm for table retrieval, bridging the gap between fine-tuned architectures and lightweight, plug-and-play retrieval systems. |
Adarsh Singh; Kushal Raj Bhandari; Jianxi Gao; Soham Dan; Vivek Gupta; | acl | 2026-06-27 |
| 300 | Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, Chart-based MRAG, to address this limitation. |
JIANG ZHONG et. al. | acl | 2026-06-27 |
| 301 | Biomedical Question Answering Via Multi-Level Summarization on A Local Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel method CLAIMS, which utilizes propositional claims to construct a local knowledge graph from retrieved documents. |
Lingxiao Guan; Yuanhao Huang; Jie Liu; | acl | 2026-06-27 |
| 302 | SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Search-AuGmented Evaluation (SAGE), a framework to assess LLM outputs without fixed ground-truth answers. |
Sher Badshah; Ali Emami; Hassan Sajjad; | acl | 2026-06-27 |
| 303 | Question Difficulty Estimation for Large Language Models Via Answer Plausibility Scoring Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce Q-DAPS (Question Difficulty based on Answer Plausibility Scores) method, a novel approach that estimates question difficulty by computing the entropy of plausibility scores over candidate answers. |
Jamshid Mozafari; Bhawna Piryani; Adam Jatowt; | acl | 2026-06-27 |
| 304 | KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce KG-MuLQA (Knowledge-Graph-based Multi-Level Question-Answer Extraction): a framework that (1) extracts QA pairs at multiple complexity levels (2) along three key dimensions – multi-hop retrieval, set operations, and answer plurality, (3) by leveraging knowledge-graph-based document representations. |
NIKITA TATARINOV et. al. | acl | 2026-06-27 |
| 305 | LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked By Knowledge Points Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a KP-graph-based synthesis framework that for the first time enables flexible control over discipline and difficulty distributions while balancing KP coverage and popularity. |
XUEMIAO ZHANG et. al. | acl | 2026-06-27 |
| 306 | CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CAMEC (Complexity-Aware Multi-Expert Collaboration), a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA. |
YUKANG WU et. al. | acl | 2026-06-27 |
| 307 | AraVQA: Building A New Arabic Factoid Visual Question Answering Dataset from Wikipedia Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In addition, most of the existing Arabic VQA datasets focus on culturally-specific and dialect-aware domains. To address these limitations, we propose a new pipeline that leverages Wikipedia template tags to extract the relevant information for each image, which is subsequently utilized by the Large Language Model (LLM) to synthetically generate a new visual question answering dataset. |
Sultan Alrowili; Younes Samih; Abed Alhakim Freihat; Mathan Kumar Eswaran; | acl | 2026-06-27 |
| 308 | SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SAHM, a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning. |
RANIA ELBADRY et. al. | acl | 2026-06-27 |
| 309 | Know The Known and The Unknown: Reasonable Answer Generation with Knowledge-Informed Citations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they often overlook key challenges such as citation granularity, the awareness of unknown information, and the adoption of effective training strategies. In this paper, we introduce Knowledge-informed Citation (KFC), which addresses these issues through a novel data construction pipeline, a new benchmark, and an innovative training strategy. |
YICHI ZHANG et. al. | acl | 2026-06-27 |
| 310 | Beyond Timestamps: Bridging Forward and Backward Reasoning in Temporal Numerical and Relational Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a bi-directional evaluation framework consisting of forward generation via Question Answering (QA) and backward verification via Fact Verification (FV). |
XINYING QIAN et. al. | acl | 2026-06-27 |
| 311 | LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose LoVeC (Long-form Verbalized Confidence), a novel reinforcement learning (RL)–based method that trains LLMs to append an on-the-fly numerical confidence score to each generated statement during long-form generation. |
Caiqi Zhang; Xiaochen Zhu; Chengzu Li; Nigel Collier; Andreas Vlachos; | acl | 2026-06-27 |
| 312 | AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, we introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). |
HONG TING TSANG et. al. | acl | 2026-06-27 |
| 313 | POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Charts are a universally adopted medium for data communication, yet existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. To address this limitation, we introduce PolyChartQA, the first large-scale multilingual benchmark for chart question answering, comprising 22,606 charts and 26,151 QA pairs across 10 diverse languages. |
YICHEN XU et. al. | acl | 2026-06-27 |
| 314 | LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This often results in incomplete evidence retrieval and degraded answer quality for multi-page reasoning tasks. To address these limitations, we propose LAD-RAG, a novel Layout-Aware Dynamic RAG framework. |
ZHIVAR SOURATI et. al. | acl | 2026-06-27 |
| 315 | CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching and fail to reflect the granular diagnostic accuracy required for clinical use. To address this gap, we propose CT-FineBench, a benchmark built from CT-RATE and Merlin to evaluate the fine-grained factual consistency of CT reports, constructed from CT-RATE and Merlin. |
RUIFENG YUAN et. al. | acl | 2026-06-27 |
| 316 | AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose AgentRouter, a framework that formulates multi-agent QA as a knowledge-graph–guided routing problem supervised by empirical performance signals. |
ZHEYUAN ZHANG et. al. | acl | 2026-06-27 |
| 317 | MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Long-tail Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce MINTQA (Multi-hop Question Answering on New and Tail Knowledge), a benchmark designed to evaluate multi-hop QA performance on questions involving 10,479 question-answer pairs for evaluating old/new knowledge and 17,887 pairs for assessing popular/unpopular knowledge, with each question equipped with corresponding sub-questions and answers. |
Jie He; Nan Hu; Wanqiu Long; Jiaoyan Chen; Jeff Z. Pan; | acl | 2026-06-27 |
| 318 | THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by Theta-Gamma hierarchical oscillation which decouples global planning from local retrieval, enabling efficient attention transfer between hops and a verification and repair mechanism that interrupts the accumulation of errors in the wrong paths, we present **THOR**, a brain-inspired Theta-Gamma hierarchical oscillatory reasoning framework. |
Ziyang Ling; Ronald X. Xu; Mingzhai Sun; | acl | 2026-06-27 |
| 319 | ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibiting underspecified or uncertain expressions. To address this, we introduce the ODUTQA-MDC task and the first comprehensive benchmark to tackle it. |
ZHENSHENG WANG et. al. | acl | 2026-06-27 |
| 320 | Revisiting Evaluation of Question Answering Systems in Low-Resource Indic Languages: Bridging Human and Metric Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These metrics often exhibit issues such as compressed scoring ranges, excessive zero scores, and weak alignment with human judgments. To overcome these limitations, this work introduces the LRM2QAS (Language Robust Multi-aspect Metrics for Question Answering Systems). |
Anuj Kumar; Satyadev Ahlawat; Yamuna Prasad; Virendra Singh; | acl | 2026-06-27 |
| 321 | Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose *PieTa* (Piece of Table), a divide-and-conquer subtable selection framework that progressively aggregates locally selected evidence without requiring explicit global reasoning. |
Wonjin Lee; Kyumin Kim; Sungjae Lee; Jihun Lee; Kwang In Kim; | acl | 2026-06-27 |
| 322 | Multimodal Graph RAG for Long-range Visually Rich Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing LLM-based KG construction methods handle only the language modality, leaving the automatic creation of multimodal KGs (MMKGs) for visually rich documents largely unexplored. In this paper, we introduce a multimodal graph-based RAG approach to tackle this problem. |
Yi-Cheng Wang; Chu-Song Chen; | arxiv-cs.IR | 2026-06-27 |
| 323 | Interpretable Traces, Unexpected Outcomes: Investigating The Disconnect in Trace-Based Knowledge Distillation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To isolate the effect of trace semantics, we design experiments in the Question Answering (QA) domain using a rule-based problem decomposition method. This enables us to create Supervised Fine-Tuning (SFT) datasets for LLMs where – each QA problem is paired with either verifiably correct or incorrect CoT traces, while always providing the correct final solution. |
Siddhant Bhambri; Upasana Biswas; Subbarao Kambhampati; | acl | 2026-06-27 |
| 324 | Answering The Wrong Question: Reasoning Trace Inversion for Abstention in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Taking the vulnerabilities of reasoning models into account, we propose our Query Misalignment Framework. |
Abinitha Gourabathina; Inkit Padhi; Manish Nagireddy; Subhajit Chaudhury; Prasanna Sattigeri; | acl | 2026-06-27 |
| 325 | MUSEG: Reinforcing Video Temporal Understanding Via Timestamp-Aware Multi-Segment Grounding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose **MUSEG**, a novel RL-based method that enhances temporal understanding by introducing timestamp-aware multi-segment grounding. |
FUWEN LUO et. al. | acl | 2026-06-27 |
| 326 | Knowledge Is Not Enough: Injecting RL Skills for Continual Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We empirically observe that the parameter updates induced by SFT and RL are nearly orthogonal. Based on this observation, we propose **Parametric Skill Transfer (PaST)**, a framework that supports modular skill transfer for efficient and effective knowledge adaptation. |
Pingzhi Tang; Yiding Wang; Muhan Zhang; | acl | 2026-06-27 |
| 327 | PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented approaches partially address this gap but lack mechanisms to iteratively refine poor queries, whereas self-reflection methods kick in only after full retrieval is completed. In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: **self-critic query refinement** evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata) retrieval; **reflective retrieval** processes articles in batches until sufficient evidence is gathered; and **evidence-grounded response generation** produces answers with explicit citations. |
Yiqing Zhang; Xiaozhong Liu; Fabricio Murai; | acl | 2026-06-27 |
| 328 | Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation Via MCTS-Guided Reasoning Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While promising, this task is particularly demanding due to the limited number of QA records available for each student, which are insufficient for training, as well as the absence of their underlying reasoning process. To overcome this, we propose a novel, training-free two-stage framework. |
TAO WU et. al. | acl | 2026-06-27 |
| 329 | S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose S2G-RAG (Structured Sufficiency and Gap-judging RAG), an iterative framework with an explicit controller, S2G-Judge. |
Minghan Li; Junjie Zou; Xinxuan Lv; Chao Zhang; Guodong Zhou; | acl | 2026-06-27 |
| 330 | What Question Did You Answer? Refining Contact Center Evaluation Plans Via Backward Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Backward Question-based Refinement (BQR), a diagnostic framework that generates backward questions, revealing what a model understood rather than what was asked, to systematically distill implicit reasoning from large LMs into explicit evaluation plans. |
Prajwal Sood; Rushikesh Pawar; Digvijay Anil Ingle; Anup Pattnaik; | acl | 2026-06-27 |
| 331 | Latent Bridges for Multi-Table Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. |
Simone Varriale; Tamara Cucumides; Floris Geerts; Paolo Papotti; | arxiv-cs.CL | 2026-06-27 |
| 332 | ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ProMSA, a progressive multimodal search agent for KB-VQA. |
ZHENGXIAN WU et. al. | arxiv-cs.CV | 2026-06-26 |
| 333 | KG2Code: Bridging Knowledge Graphs and Large Language Models Via Executable Code for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although these methods have achieved notable success, they still suffer from several limitations, including structural information loss, unfaithful reasoning, and limited flexibility and generalization. To address these challenges, this paper proposes KG2Code, a novel approach that transforms knowledge graphs into a code-based representation, preserving structural semantics while naturally aligning with the code-aware pretraining of modern LLMs. |
YIKE WU et. al. | arxiv-cs.AI | 2026-06-26 |
| 334 | When The Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we explore the potential vulnerability where a malicious aggregator, who may collude with a third-party vendor, stealthily implants advertisement-type backdoors into federated QA models, without ever accessing client data. |
Chenqing Zhu; Yanbo Dai; Yulong Tian; Qingming Li; Songze Li; | arxiv-cs.CR | 2026-06-25 |
| 335 | Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper describes team HSA_CORAL’s submission to the FinCausal 2026 shared task on extracting cause-effect relations from financial narratives via extractive question answering in English and Spanish. |
Akash Kumar Gautam; Serhii Hamotskyi; Christian Hänig; | arxiv-cs.CL | 2026-06-25 |
| 336 | TriViewBench: Controlled Complexity Scaling for Multi-View Structural Reasoning in MLLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce TriViewBench, a controlled three-view visual reasoning benchmark constructed from synthetic 3D scenes with explicitly parameterized object count and occlusion. |
Yu-Yang Chen; Lan-Zhe Guo; | arxiv-cs.CV | 2026-06-24 |
| 337 | Automatic Prompt Generation Via Semantic Decomposition-and-recomposition for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Seungyeon Lee; Dong-Gyu Lee; | Engineering Applications of Artificial Intelligence | 2026-06-24 |
| 338 | Corrigendum to “Multi-aspect Attentive Text Representations for Simple Question Answering Over Knowledge Base” [Natural Language Processing Journal, Volume 5, (2023), 100035] Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Zhixiang Zeng; Yuefeng Li; Jianming Yong; Xiaohui Tao; Vicky Liu; | Natural Language Processing Journal | 2026-06-23 |
| 339 | Video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present video-SALMONN-R$^3$, the first end-to-end video-LLM that enables re-watch through reinforcement learning without relying on chain-of-thought (CoT) cold-start. |
YIXUAN LI et. al. | arxiv-cs.CV | 2026-06-23 |
| 340 | EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are … |
Linpeng Huang; Weixing Chen; Zexin Chen; Yang Liu; Liang Lin; | arxiv-cs.CV | 2026-06-23 |
| 341 | CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This produces two failure modes: committing to confident but unsupported answers, which hurts accuracy, and over-retrieving when the evidence in hand already suffices, resulting in wasted compute. To give agents a more complete picture of the state space they are operating in, we introduce calibrated verifier telemetry (CalVerT), which augments the agent’s state with additional telemetry: a calibrated self-confidence score and a grounding verifier score. |
Ashwin Vinod; Ying Ding; Elias Stengel-Eskin; | arxiv-cs.CL | 2026-06-19 |
| 342 | AdaMem: Learning What to Remember for Personalized Long-Horizon LLM Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We argue that what is worth remembering is role-dependent, and propose \textbf{AdaMem} (Adaptive Memory), a method that \emph{learns what to remember} for each user from feedback. |
Xingyu Chen; Rui Wang; Zhaopeng Tu; Liefeng Bo; | arxiv-cs.CL | 2026-06-19 |
| 343 | AgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present AgentFinVQA, a multi-agent pipeline that decomposes each query into planning, OCR, legend grounding, visual inspection, and verification, recording every step in a traceable Model Evaluation Packet (MEP) per sample. |
Aravind Narayanan; Shaina Raza; | arxiv-cs.AI | 2026-06-18 |
| 344 | Fine-grained Human Motion Understanding with Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose \methodname, an LLM-based model for fine-grained human motion understanding that represents motion as a sequence of skeletal poses with explicit timestamps for each pose. |
Thomas Markhorst; Zhi-Yi Lin; Jouh Yeong Chew; Jan van Gemert; Xucong Zhang; | arxiv-cs.CV | 2026-06-18 |
| 345 | WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce WorldLines, a project-driven benchmark for long-horizon embodied household assistance. |
YEHANG ZHANG et. al. | arxiv-cs.AI | 2026-06-17 |
| 346 | Code-Switching Reveals Language Anchoring in Multilingual LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To understand this degradation, we use grammar-forced CS as a controlled diagnostic setting for locating CS representations relative to their source and target counterparts. We introduce Anchor Bias, a geometric measure that quantifies language anchoring, whether a CS hidden state aligns closer to its source or target language counterpart. |
Jeonghyun Park; Seunghyun Yoon; Yonghyun Jun; Hwanhee Lee; | arxiv-cs.CL | 2026-06-17 |
| 347 | Optimal Scheduling in A Question-Answering Forum of Knowledge Workers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: With this model, we calculate the capacity of the system for handling the requests while keeping the system stable, and design schedulers that achieve capacity. |
Rohit Negi; Mustafa Yilmaz; | arxiv-cs.AI | 2026-06-17 |
| 348 | Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a study of medical domain adaptation using French medical question-answering (QA) as a case study. |
IKRAM BELMADANI et. al. | arxiv-cs.CL | 2026-06-17 |
| 349 | Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present OPTI-Q, a database-inspired, cost-based optimizer that implements a plan-before-execute paradigm for multi-LLM orchestration. |
AAMIR HAMID et. al. | arxiv-cs.AI | 2026-06-16 |
| 350 | From Drift to Coherence: Stabilizing Beliefs in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce prompted predictive resampling (PPR), where an LLM generates a sequence of answers to the same question. |
SongEun Kim; Seungyoo Lee; Edwin Fong; Hyungi Lee; Juho Lee; | arxiv-cs.LG | 2026-06-16 |
| 351 | Multi-Modal Generative Fuzzy System: Fuzzy Inference Guided Large Model Interactive Question Answering Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Third, current methods often rely on shallow semantic matching, resulting in limited reasoning depth an reduced interpretability. To address these issues, inspired by the traditional fuzzy system (FS) framework, we propose a fuzzy-inference-guided multimodal generative architecture termed the Multi-Modal Generative Fuzzy System (MMGFS). |
Hailong Yang; Jianqi Wang; Guanjin Wang; Zhaohong Deng; | arxiv-cs.CL | 2026-06-16 |
| 352 | Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work proposes BinTrack, a simple yet effective, fully open-source spatial-localization agent that leverages the temporal ordering of a robot’s trajectory. |
DONGBIN NA et. al. | arxiv-cs.RO | 2026-06-15 |
| 353 | JARVIS for HVAC: LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present JARVIS , a two-stage LLM-based QA framework tailored for sensor data-driven HVAC system interaction. |
SUNGMIN LEE et. al. | Proceedings of the ACM on Interactive, Mobile, Wearable and … | 2026-06-15 |
| 354 | Weaving Multi-Source Evidence for Biomedical Reasoning: The BioMedHop Benchmark and BioWeave Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing biomedical QA benchmarks mainly focus on exam-style knowledge, literature comprehension, or short-range multi-hop inference, leaving source-conditioned graph reasoning and evidence topology construction underexplored. To fill this gap, we introduce BioMedHop, a multi-source graph-grounded benchmark for evaluating biomedical reasoning over structured evidence topologies. |
XINGYU TAN et. al. | arxiv-cs.CL | 2026-06-15 |
| 355 | A Self Consistency Based Reranking for Narrative Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite recent advances in pretrained language models, most existing approaches rely on a single decoding output during inference, making them sensitive to generation variability and often resulting in incomplete or inconsistent answers . To address this limitation, we propose a self-ensemble Self-Consistency-Based reranking framework for narrative question answering. |
Molham Mohamed; Ali Hamdi; | arxiv-cs.CL | 2026-06-14 |
| 356 | EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering Over Longitudinal Discharge Summaries Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce EHRNote-ChatQA, the first benchmark for evidence-grounded multi-turn clinical question answering over patients’ multiple discharge summaries. |
JIYOUN KIM et. al. | arxiv-cs.CL | 2026-06-14 |
| 357 | MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes MAGE-RAG, a multigranular adaptive graph evidence framework for long-document multimodal QA. |
YILONG ZUO et. al. | arxiv-cs.IR | 2026-06-14 |
| 358 | Object Tokens As A Bridge Between Segmentation and Visual Question Answering in Robotic Surgery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a unified framework that jointly performs pixel-level segmentation and visual question answering within a single framework. |
YIPING LI et. al. | arxiv-cs.CV | 2026-06-14 |
| 359 | Medical Visual Question Answering with Multimodal: A Systematic Mini Review (2023–2026) Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study aims to systematically analyze recent developments in Med-VQA. |
MAIMUNA BISWAS NOSHIN et. al. | Frontiers in Digital Health | 2026-06-12 |
| 360 | OmniVideo-100K: A Dataset for Audio-Visual Reasoning Through Structured Scripts and Evidence Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, coupling long-text comprehension and QA synthesis into a single step often restricts models to localized events, yielding questions lacking long-term temporal connections and deep cross-modal reasoning. To address these issues, we propose an automated data engine featuring two mechanisms: (1) \textbf{Entity-Anchored Video Scripting} transforms videos into structured scripts, comprising summaries, main entity lists, and segment-wise audio-visual descriptions. |
Xinyue Cai; Chaoyou Fu; Yi-Fan Zhang; Ran He; Caifeng Shan; | arxiv-cs.CV | 2026-06-12 |
| 361 | ReportQA: QA-Based Radiology Report Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Clinicians use them to perform downstream diagnostic tasks without directly inspecting images. Based on this insight, we propose ReportQA, a clinical-related and flexible radiology report evaluation framework, supporting detailed quantitative analysis of radiology report generation systems. |
YIMING SHI et. al. | arxiv-cs.CL | 2026-06-12 |
| 362 | HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose HyCE-RAG, a Hypergraph Chain-of-Evidence Retrieval-Augmented Generation framework for explainable multi-hop question answering. |
HONG-YU AN et. al. | arxiv-cs.AI | 2026-06-12 |
| 363 | Operads for Compositional Reasoning in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose operads, mathematical structures that model many-in, one-out operations and compositions thereof, as a natural framework for describing question decomposition. |
Nathaniel Bottman; Kyle Richardson; | arxiv-cs.CL | 2026-06-11 |
| 364 | An Inference Framework for Integrating Visual Question Answering and Object Distance Estimation on Monocular Images Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Joon Cho; Choulsoo Jang; Changeun Lee; Kyunam Kim; | The Journal of Korea Robotics Society | 2026-06-11 |
| 365 | Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. |
HAOYANG ZENG et. al. | arxiv-cs.AI | 2026-06-10 |
| 366 | One Token Per Multimodal Evidence: Latent Memory for Resource-Constrained QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Latent Memory, a latent-space memory paradigm that replaces each raw text or image evidence item with a single high-dimensional latent token produced by a small compressor LLM/VLM. |
Zhi Zheng; Ziqiao Meng; Hao Luan; Wei Liu; Wee Sun Lee; | arxiv-cs.AI | 2026-06-09 |
| 367 | LakeQA: An Exploratory QA Benchmark Over A Million-Scale Data Lake Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we introduce LakeQA, a comprehensive benchmark for search-centric question answering over data lakes that jointly emphasizes searching and reasoning capabilities. |
HAONAN WANG et. al. | arxiv-cs.CL | 2026-06-09 |
| 368 | ScoutVLA: UAV-Centric Active Perception Via A Dual-Expert VLA Model for Open-World Embodied Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Drawing inspiration from the “waggle dance” of scout bees, which iteratively adjust their flight paths to verify target information, we propose ScoutVLA, an evidence-driven Vision-Language-Action model for outdoor EQA. |
WENHAO LU et. al. | arxiv-cs.CV | 2026-06-09 |
| 369 | Comparison of Three Large Language Models in Postoperative Rehabilitation Question Answering After Anterior Cruciate Ligament Reconstruction Based on Expert Ratings Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
TIANGE XIA et. al. | BMC Musculoskeletal Disorders | 2026-06-09 |
| 370 | Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although retrieval-augmented generation (RAG) reduces the input context by retrieving relevant evidence, existing structured RAG methods still face three limitations: costly query-agnostic knowledge organization, insufficient use of original document structure, and no reuse of historical reasoning experience. To address these limitations, we propose DocTrace, a multi-agent RAG framework for long-document QA that supports query-triggered knowledge organization, document-structure-aware and experience-guided reasoning. |
Xiangjun Zai; Xingyu Tan; Chen Chen; Xiaoyang Wang; Wenjie Zhang; | arxiv-cs.CL | 2026-06-09 |
| 371 | TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce TABVERSE, a controlled multimodal table benchmark that aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags. |
Momina Ahsan; Sarfraz Ahmad; Ming Shan Hee; Roy Ka-Wei Lee; Preslav Nakov; | arxiv-cs.AI | 2026-06-08 |
| 372 | Where Does The Answer Come From? Benchmarking View-Level Visual Evidence Identification in Multi-View MLLMs for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a multi-view visual question answering benchmark for evaluating evidence-source identification: given six synchronized NuScenes views and a question, the model must identify the supporting camera view and answer the question. |
YIMU WANG et. al. | arxiv-cs.CL | 2026-06-08 |
| 373 | MMClima: A Framework for Multimodal Climate Science Data and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce MMClima, a large-scale multimodal climate question answering framework with 104k+ expert-validated question-answer pairs spanning articles, video transcriptions, and figures across five core climate science domains. |
Muhammad Umer Sheikh; Hassan Abid; Khawar Shehzad; Ufaq Khan; Muhammad Haris Khan; | arxiv-cs.LG | 2026-06-08 |
| 374 | ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. |
YI ZHANG et. al. | arxiv-cs.CV | 2026-06-07 |
| 375 | Ibcl: Instance-aware Bias Calibration with Contrastive Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Chuanfeng Liu; Hao Wu; Benxue Sun; Zhijun Fang; | Multimedia Systems | 2026-06-06 |
| 376 | SPARC: A Multi-Agent System for Electrical Circuit Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SPARC, a multi-agent system that answers questions over circuit diagrams by grounding reasoning in executable physics-based simulations. |
MUSHTARI SADIA et. al. | arxiv-cs.AI | 2026-06-05 |
| 377 | CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answering), a deterministic tool-grounded multi-agent LLM framework for financial question answering. |
Kunjesh Parekh; Anil Kumar Tiwari; Divya Saxena; | arxiv-cs.AI | 2026-06-05 |
| 378 | A COMPARATIVE EVALUATION OF LARGE LANGUAGE MODELS FOR PATIENT-FACING MEDICAL QUESTION ANSWERING: ACCURACY, BIAS, AND CLINICAL SAFETY ASSESSMENT Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Vinodhini Ravikumar; | INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN MEDICINE | 2026-06-05 |
| 379 | Scene Graph-guided Uncertainty Decomposition Improves Confidence Calibration in Surgical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods typically model predictive uncertainty as a single global quantity, which fails to capture the hierarchical uncertainty arising from ambiguous objects, uncertain tool–tissue interactions, and complex scene context. To address this limitation, we propose a scene graph-guided framework that decomposes uncertainty into object-level, relation-level, and scene-level components and adaptively fuses them for confidence-aware surgical visual question answering. |
JUNZHUO SONG et. al. | Frontiers in Medicine | 2026-06-05 |
| 380 | Explicit Evidence Grounding Via Structured Inline Citation Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Properly attributing information through citations becomes, therefore, crucial. This work introduces FullCite, a framework that, in contrast to most previous works, generates structured inline citations linking each claim to both its source document and supporting evidence. |
Anar Yeginbergen; Amelie Wührl; Anna Rogers; Rodrigo Agerri; | arxiv-cs.CL | 2026-06-05 |
| 381 | EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Evidence-Aligned SElective Test-Time Training (EASE-TTT), a within-context retrieval-augmented test-time training framework that converts selected evidence chunks into a soft attention supervision target over their token positions. |
XIAOPENG YUAN et. al. | arxiv-cs.CL | 2026-06-05 |
| 382 | Improving Answer Extraction in Context-based Question Answering Systems Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a question answering system based on large language models, where the input consists of a textual context and a corresponding question, and the output is a concise and accurate answer. |
Hafez Abdelghaffar; Ahmed Alansary; Ali Hamdi; | arxiv-cs.CL | 2026-06-04 |
| 383 | Reducing Hallucinations in Complex Question Answering Using Simple Graph-based Retrieval-Augmented Generation (long Version) Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we explore the idea of using a lightweight graph structure with a relatively simple graph schema, to support the RAG subsystem via a dedicated toolset. |
Christopher J. Wedge; Joshua Stutter; Danny Dixon; Jacek Cała; | arxiv-cs.CL | 2026-06-04 |
| 384 | MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they still rely on isolated and fragmented frames as the fundamental evidence units, limiting VLMs’ ability to effectively capture coherent event-level semantics. To address this limitation, we propose MemoryCard, a video-memory-based augmentation framework that organizes long videos into self-contained Memory Cards. |
QING YANG et. al. | arxiv-cs.CV | 2026-06-04 |
| 385 | MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose MARDoc, a Memory-Aware Refinement Agent framework that decouples long-document QA into three specialized agents: an Explorer for multi-granularity multimodal retrieval, a Refiner for distilling interaction traces into structured evidence and reasoning memories, and a Reflector for checking evidence sufficiency and providing targeted feedback. |
KAIFENG CHEN et. al. | arxiv-cs.CL | 2026-06-04 |
| 386 | Chemical-Attribute Extraction Via Inverse Reinforcement Learning with Sub-Reward Matching for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study offers an efficient solution for mining implicit knowledge in chemical texts and provides insights into multi-objective generative tasks. |
Taiyu Zhang; Yuqing Ni; Xicheng Yang; Congyuan Xu; Xiaochen Liu; | Applied Sciences | 2026-06-03 |
| 387 | Hierarchical Context Enhancement for Long-tail Entity Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our analysis reveals that the main cause is the absence of a dedicated mechanism for handling low-frequency terms. |
Yixuan Peng; Kewu Pan; | Frontiers in Artificial Intelligence | 2026-06-02 |
| 388 | Can Small Language Models Handle Context-summarized Multi-turn Customer-service QA? A Synthetic Data-driven Comparative Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The main contributions of this work include the application of parameter-efficient fine-tuning to adapt SLMs for context-summarized multi-turn customer-service QA, a synthetic data construction pipeline for generating a context-summarized multi-turn QA dataset, and a structured evaluation framework combining quantitative metrics with human and LLM-as-a-judge assessments for customer-service QA evaluation. |
Lakshan Cooray; Deshan Sumanathilaka; Pattigadapa Venkatesh Raju; | Frontiers in Artificial Intelligence | 2026-06-02 |
| 389 | When Retrieval Doesn’t Help: A Large-Scale Study of Biomedical RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Across five models, ten biomedical QA datasets, four retrieval methods, and four retrieval corpora, we find that retrieval yields only small and inconsistent improvements over a no-retrieval baseline, typically within 1-2 points. |
Erfan Nourbakhsh; Rocky Slavin; Ke Yang; Anthony Rios; | arxiv-cs.CL | 2026-06-02 |
| 390 | Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce IndoRad-VQA, an Indonesian adaptation of VQA-RAD, to assess whether medical VLMs retain radiology reasoning ability when questions are asked in Bahasa Indonesia. |
Pieter Christy Yan Yudhistira; Dzaki Rafif Malik; Novanto Yudistira; | arxiv-cs.CL | 2026-06-02 |
| 391 | Adaptive Retrieval Strategies for Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose an adaptive retrieval framework that selects retrieval and evidence aggregation strategies according to question type. |
HAN YUE et. al. | arxiv-cs.IR | 2026-06-02 |
| 392 | HC-RAG: Evidence-Centric Retrieval-Augmented Generation Over Heterogeneous Financial Filings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing RAG systems, however, usually flatten long filings into unordered chunks, pay limited attention to the typed structure of financial reports, and use fixed text-table fusion strategies without considering query intent. To address these limitations, we propose \textbf{HC-RAG}, a hierarchical cross-modal retrieval-augmented generation framework for evidence-centric financial QA. |
Siyuan Chen; Huaye Tan; You Li; Jiajun Liang; | arxiv-cs.CL | 2026-06-02 |
| 393 | RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce RASER (Recoverability-Aware Selective Escalation Router), a family of cheap routers built on one-shot RAG and six features from it. |
Yuyang Li; Zihe Yan; Tobias Käfer; | arxiv-cs.AI | 2026-06-01 |
| 394 | ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This task poses challenges in retrieving precise historical data, overcoming the forecasting limitations of LLMs, and standardizing responses for diverse queries. To solve the above challenges, we propose TimeFore, an LLM agent-based framework that decomposes the problem into three collaborative roles: a Retriever autonomously generates SQL to fetch data, a Forecaster invokes external time-series models for higher accuracy, and an Analyzer synthesizes the results to construct a precise and consistent final answer. |
ZHENSHENG WANG et. al. | arxiv-cs.IR | 2026-06-01 |
| 395 | InsightVQA: High-Dimensional Emotion-Cognitive Visual Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing benchmarks mainly focus on emotion recognition, offering limited support for grounded understanding and response-oriented analysis. To address this gap, we introduce \textbf{InsightVQA}, a large-scale dataset for hierarchical visual question answering on emotion understanding and cognitive reasoning. |
SHIYU WANG et. al. | arxiv-cs.CV | 2026-06-01 |
| 396 | A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We show that single-domain RL produces sparse, small-magnitude parameter edits with weak overlap among top-changed neurons, while different domains still share substantial active computation routes on which update directions determine whether they act synergistically or conflict. Guided by this observation, we prove under a local perturbation model of multi-domain RL that later-domain training harms an earlier domain mainly through a second-order damage term, which under the observed sparse route structure concentrates in a low-dimensional shared conflict subspace. |
Lei Yang; Siyu Ding; Deyi Xiong; | arxiv-cs.LG | 2026-06-01 |
| 397 | Flow-RAG: Retrieval-augmented Generation for Knowledge Graph Question Answering Via Gated Flow Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Weina Zhang; Junsheng Huang; Zhongqin Bi; Baonan Wang; | Knowl. Based Syst. | 2026-06-01 |
| 398 | Question-Aware Evidence Ledgers for Video Relational Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a test-time reasoning pipeline built around a strong GPT-5.5 video QA solver and a set of question-aware evidence ledgers. |
Yilin Ou; Mengshi Qi; Huadong Ma; | arxiv-cs.CV | 2026-06-01 |
| 399 | Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present an inference-only system built around adaptive test-time computation. |
YUYANG SUN et. al. | arxiv-cs.CV | 2026-05-31 |
| 400 | Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video. |
Ali Alavi; | arxiv-cs.CV | 2026-05-31 |
| 401 | OCC-RAG: Optimal Cognitive Core for Faithful Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this setting, task-specialized small language models (SLMs) offer a principled design choice. We introduce Optimal Cognitive Core (OCC), a family of SLMs built around this premise. |
MAKSIM SAVKIN et. al. | arxiv-cs.CL | 2026-05-30 |
| 402 | SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose SPADER, a reinforcement learning framework for long-horizon tool use in Multi-Answer QA. |
Qiming Shi; Zhaolu Kang; Yunfan Zhou; Di Weng; Yingcai Wu; | arxiv-cs.CL | 2026-05-30 |
| 403 | HypothesisMed: Inference-Time Answer Fusion and Structured Hypothesis-Space Reporting for Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents HypothesisMed, an inference-time reliability pipeline for biomedical multiple-choice question answering. |
Md Motaleb Hossen Manik; Ge Wang; | arxiv-cs.CL | 2026-05-30 |
| 404 | Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In high-stakes domains, these errors can reduce trust and introduce real-world risk. To address this challenge, we present a parameter-efficient approach that uses soft prompts to mitigate hallucinated content and promote responsible abstention in generative question-answering (QA) tasks. |
S M Tahmid Siddiqui; Akib Jawad Ononto; Anoop Singhal; Latifur Khan; | arxiv-cs.CL | 2026-05-30 |
| 405 | KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We formulate hallucination detection in KBQA as an answer-node classification problem and propose a lightweight graph-based framework that treats the answering LLM as a black box. |
Albert Sawczyn; Piotr Bielak; Tomasz Kajdanowicz; | arxiv-cs.LG | 2026-05-29 |
| 406 | AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This overlooks the dynamic nature of multi-step tasks, where the need for explicit reasoning varies across intermediate stages. To address this limitation, we introduce AdaptR1, a Reinforcement Learning (RL) based framework for adaptive interleaved thinking in multi-hop Question Answering (QA). |
YUXIN WANG et. al. | arxiv-cs.CL | 2026-05-29 |
| 407 | Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Semantic Triplet Restoration (STR), a protocol that rewrites each cell as an atomic fact |
Yibin Zhao; Fangxin Shang; Dingrui Yang; Yuqi Wang; | arxiv-cs.CL | 2026-05-29 |
| 408 | Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CorVer (Corpus Verify), a lightweight, plug-in-ready process reward that replaces neural verifiers with a corpus-grounded signal derived from Wikipedia co-occurrence statistics. |
SHICHENG FAN et. al. | arxiv-cs.CL | 2026-05-28 |
| 409 | Brain-IT-VQA: From Brain Signals to Answers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Brain-IT-VQA, a framework for visual question answering from fMRI. |
Roman Beliy; Matias Cosarinsky; Oliver Heinimann; Navve Wasserman; Michal Irani; | arxiv-cs.CV | 2026-05-28 |
| 410 | VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring Over Wearable Health Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose VitalAgent, a tool-augmented agentic framework for ECG/PPG-based mHealth that supports both reactive question answering and proactive monitoring. |
DI ZHU et. al. | arxiv-cs.AI | 2026-05-28 |
| 411 | OptoChat: A Large Language Model with Retrieval Augmented Generation for Optics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present OptoChat, a retrieval-augmented LLM purpose-built for the optical domain. |
XIAOQING BAO et. al. | Journal of Physics: Photonics | 2026-05-28 |
| 412 | Citation-Closure Retrieval and Per-Rule Attribution for Real-World Regulatory Compliance Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We formalize Regulatory Compliance QA with RegOps-Bench, a novel benchmark featuring an Operational Knowledge Graph derived from complex national R\&D regulations. To address these bottlenecks, we propose RefWalk, a unified framework driven by a shared topic anchor. |
Yeong-Joon Ju; Seong-Whan Lee; | arxiv-cs.AI | 2026-05-28 |
| 413 | Knowledge Dependency Estimation for Reliable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose \textbf{Knot}, a structured rank-aware knowledge dependency estimator. |
Chaodong Tong; Qi Zhang; Nannan Sun; Lei Jiang; Yanbing Liu; | arxiv-cs.CL | 2026-05-27 |
| 414 | Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction Via Ontology-grounded Post-extraction Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a neuro-symbolic framework for ontology-grounded KG construction combining open-domain extraction, embedding-based canonicalization of types and predicates, and targeted LLM-based correction of ontology violations. |
Lorenzo Loconte; Timothy Hospedales; Cristina Cornelio; | arxiv-cs.AI | 2026-05-27 |
| 415 | Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present CrossAug, a GNN-guided CROSS-Chunk Graph AUGmentation method that enriches GraphRAG indices with cross-chunk relational structure as an offline step before query-time retrieval. |
Jiaming Zhang; Yibo Zhao; Jing Yu; Jianxiang Yu; Xiang Li; | arxiv-cs.CL | 2026-05-27 |
| 416 | DocArena: Turning Raw Documents Into Controllable Training Environments for Document Search Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While prior works have made encouraging progress in improving training data quality, existing environments remain predominantly text-based and existing approaches can struggle to construct training environments that are controllable, scalable, and account for multimodal data. Given this, we propose DocArena, a fully automated data curation pipeline building on the practical need for multimodal document search and question-answering. |
JIAMIAN WANG et. al. | arxiv-cs.CV | 2026-05-27 |
| 417 | ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose ConRAG, a consensus-driven multi-view RAG framework that effectively boosts LLMs on complex multi-hop QA. |
Yikai Zhu; Kunfeng Chen; Qihuang Zhong; Juhua Liu; Bo Du; | arxiv-cs.CL | 2026-05-27 |
| 418 | EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we systematically investigate the roles of positive and negative samples in reinforcement learning for open-ended QA. |
YUNSHENG ZENG et. al. | arxiv-cs.AI | 2026-05-26 |
| 419 | Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Granuscore, a reference-free measure of granularity that leverages structural properties of a hierarchical embedding space. |
Lukas Ellinger; Alexander Fichtl; Miriam Anschütz; Georg Groh; | arxiv-cs.CL | 2026-05-26 |
| 420 | Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Chartographer, a framework to reverse engineer charts into executable code, validate reconstruction fidelity, generate seed-controlled counterfactual variants, and derive new answers from executable QA logic. |
Yifan Jiang; Dae Yon Hwang; Jesse C. Cresswell; Freda Shi; | arxiv-cs.CL | 2026-05-26 |
| 421 | ForestHG-Trace: Traceable Long-Horizon Ecological Reasoning Over Large-Scale Forest Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ForestHG-Trace, a framework for traceable long-horizon ecological reasoning over forest environments. |
ZIHANG CHENG et. al. | arxiv-cs.CV | 2026-05-26 |
| 422 | Extending Embodied Question Answering from Perception to Decision Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present EQA-Decision, a large-scale embodied QA dataset that systematically covers four complementary dimensions of embodied reasoning: static scene construction, spatial understanding, task dynamics reasoning, and instant decision. |
Xicheng Gong; Qiwei Li; Peiran Xu; Yadong Mu; | arxiv-cs.RO | 2026-05-25 |
| 423 | MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering Via Miscoverage Risk Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce MiRD, a two-stage framework that decomposes overall miscoverage into sampling failure and conditional selection failure. |
Anqi Hu; Zhiyuan Wang; Zijun Jia; Bo Fu; | arxiv-cs.CL | 2026-05-25 |
| 424 | Clarification Is Not Enough: Post-Clarification Answering Remains The Bottleneck in Multi-Turn QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study the problem of preference elicitation in multi-turn question answering by decomposing the problem into two components: a \textbf{clarification policy}, which decides whether to ask a clarifying question or answer directly, and \textbf{post-clarification answering}, which produces the correct final answer once the missing information is provided. |
Jinyan Su; Jennifer Healey; | arxiv-cs.CL | 2026-05-24 |
| 425 | Knowing But Not Showing: LLMs Recognize Ambiguity But Rarely Ask Clarifying Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Doing so requires two abilities: recognizing that a query is ambiguous, and acting on that recognition by seeking clarification instead of answering directly. To study these abilities, we evaluate models on ambiguous, unambiguous, and disambiguated questions in three settings: standard question answering, explicit ambiguity judgment, and behavioral analysis, where a judge model classifies responses as direct answers, refusals, or clarifying questions. |
Jinyan Su; Claire Cardie; | arxiv-cs.CL | 2026-05-24 |
| 426 | AstroRAG — A Pagerank-Based Retrieval-Augmented Generation Pipeline for Question Answering in Astronomy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present AstroRAG — a PageRank-based retrieval-augmented generation (RAG) pipeline adapted for question answering in astronomy. |
Zhifeng Wang; Jason Jingshi Li; Kaihao Zhang; Ramesh Sankaranarayana; | arxiv-cs.CV | 2026-05-24 |
| 427 | CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, progress in privacy-preserving QA is hindered by the lack of annotated, context-rich datasets capable of jointly evaluating operational reasoning and privacy preservation. To address this gap, we introduce CYBERMASKQA, a privacy-aware QA benchmark covering key security domains. |
Matilda Gaddi; Jin Noh; Onat Gungor; Tajana Rosing; | arxiv-cs.CR | 2026-05-23 |
| 428 | StepGap: A Hybrid NLI-LLM Checker for Step-Level Evidence-Gap Detectionin Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present \textbf{StepGap}, a hybrid NLI-LLM decision tree that detects step-level evidence gaps in multi-hop QA and emits one of three typed labels: \textsc{Contradicted Claim} (CC), \textsc{Irrelevant Evidence} (IE), or \textsc{Missing Bridge} (MB), each tied to a concrete repair action. |
Yuelyu Ji; Zhuochun Li; Hui Ji; Daqing He; | arxiv-cs.CL | 2026-05-23 |
| 429 | Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite recent progress in multi-hop QA, existing approaches often rely on reasoning in natural language or retrieval without explicit query reformulation, leaving the vocabulary gap between user questions and statutory text largely unaddressed. To address this challenge, we propose Decompose-and-Refine (DaR), a statute-grounded LQA framework that tightly integrates step-wise question decomposition with parametric knowledge-based query refinement. |
Jihyung lee; Hyounghun Kim; Gary Lee; | arxiv-cs.CL | 2026-05-23 |
| 430 | TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce TS-Skill, a controlled benchmark for evaluating three composable analytical skills in TSQA: temporal scale selection (SK1), temporal localization (SK2), and cross-interval integration (SK3). |
LIYING HAN et. al. | arxiv-cs.CL | 2026-05-23 |
| 431 | Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Med-R2 Bench, a hierarchical benchmark aligned with the clinical workflow to evaluate adversarial robustness with visual grounding. |
WEN MA et. al. | arxiv-cs.CV | 2026-05-23 |
| 432 | Decomposing Queries Into Tool Calls for Long-Video Keyframe Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ToolMerge, a keyframe retrieval method based on decomposition and merging: an Large Language Model (LLM) based planner decomposes the query into tool calls and specifies how their per-tool rankings are merged using boolean operators. |
Michal Shlapentokh-Rothman; Prachi Garg; Yu-Xiong Wang; Derek Hoiem; | arxiv-cs.CV | 2026-05-22 |
| 433 | Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we present a benchmark of 312 expert-validated, time-sensitive German statutory QA pairs spanning three categories: Post-Cutoff Amendment Questions, Pre-Amendment Questions, and Multi-Provision Pre-Amendment Questions. |
Max Prior; Andreas Schultz; Matthias Grabmair; | arxiv-cs.CL | 2026-05-22 |
| 434 | DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DriveSpatial, a benchmark of 15.6K human-verified QA pairs across 20 tasks from five large-scale AD datasets. |
HAO VO et. al. | arxiv-cs.CV | 2026-05-21 |
| 435 | DeferMem: Query-Time Evidence Distillation Via Reinforcement Learning for Long-Term Memory QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This workflow leaves downstream answerers to denoise retrieved candidates and reconstruct query-specific evidence. We present DeferMem, a long-term memory framework that decouples this problem into high-recall candidate retrieval and query-conditioned evidence distillation. |
Jianing Yin; Tan Tang; | arxiv-cs.CL | 2026-05-21 |
| 436 | GS-QA: A Benchmark for Geospatial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present GS-QA, an extensible geospatial QA benchmark with 2,800 question-answer pairs across 28 templates on top of OpenStreetMap and Wikipedia data, covering a wide range of spatial objects, predicates (including directional and towards filtering), and answer types (entity names, locations, distances, directions, counts, and aggregated areas/lengths). |
Majid Saeedan; Muhammad Shihab Rashid; Ahmed Eldawy; Vagelis Hristidis; | arxiv-cs.DB | 2026-05-21 |
| 437 | MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes MuKV, a method that features a multi-grained KV cache compression module and a semi-hierarchical retrieval approach to improve both efficiency and accuracy for long streaming VideoQA. |
Junbin Xiao; Jiajun Chen; Tianxiang Sun; Xun Yang; Angela Yao; | arxiv-cs.CV | 2026-05-21 |
| 438 | OncoTriad-QA: A Patient-Level Radiology-Pathology-Genomics Benchmark for Pan-Cancer Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce OncoTriad-QA, a patient-level radiology-pathology-genomics benchmark for pan-cancer question answering. |
AHNAF MUNIR et. al. | arxiv-cs.CL | 2026-05-21 |
| 439 | $M^3 QuestionIng$: Multi-modal Multi-span Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing systems fall short of aligning with real-world scenarios, where source documents often include both textual and visual content, requiring answers to incorporate images for better comprehension. To address this gap, we propose $M^3QAFrame$, a multi-modal, multi-span medical question-answering framework that leverages visual cues to enhance the generation of comprehensive answers drawn from diverse textual and visual spans. |
ANISHA SAHA et. al. | arxiv-cs.IR | 2026-05-19 |
| 440 | SADL: Sampling, Deliberation, and Pseudo-labeling for In-context Learning in Compositional Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Long Hoang Dang; Thao Minh Le; Vuong Le; Tu Minh Phuong; Truyen Tran; | Discover Artificial Intelligence | 2026-05-19 |
| 441 | CaMo: Camera Motion Grounded Evaluation and Training for Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the Spatial Narrative Score (SNS), an evaluation framework that requires VLMs to generate explicit spatial narratives capturing both scene semantics and camera motion, followed by reasoning with a frozen proxy LLM. |
HSIANG-WEI HUANG et. al. | arxiv-cs.CV | 2026-05-19 |
| 442 | FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our extensive evaluation reveals that while proprietary models like GPT-5 achieve respectable performance, current open-source VLMs significantly underperform, struggling particularly with spatial reasoning in multi-person scenes and distinguishing subtle differences in human movements and interactions. To address these identified weaknesses, we propose FineAgent, a modular framework that enhances VLMs by leveraging a Localizer and a Descriptor. |
Gueter Josmy Faure; Min-Hung Chen; Jia-Fong Yeh; Hung-Ting Su; Winston H. Hsu; | arxiv-cs.CV | 2026-05-19 |
| 443 | NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present NeuroQA, a large-scale benchmark for visual question answering in 3D brain magnetic resonance imaging (MRI), with 56,953 QA pairs from 12,977 subjects across 12 datasets. |
MOHAMMAD H. ABBASI et. al. | arxiv-cs.CV | 2026-05-19 |
| 444 | The Evolution and Open Challenges of Text-based Visual Question Answering: A Review of Research and Data Trends Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Kobra Farshidi; Hassan Khotanlou; Elham Alighardash; | Multimedia Tools and Applications | 2026-05-18 |
| 445 | Skyline Retrieval Meets Set-Cover Chunk Merging: A Cost-Effective RAG-Sketch for Long-Context LLM QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a cost-effective Retrieval-Augmented Generation Sketch (RAG-Sketch) for long-context LLM QA. |
Xinyi Zhu; Haoyang Li; Yongqi Zhang; Lei Chen; | Proceedings of the ACM on Management of Data | 2026-05-18 |
| 446 | SVFSearch: A Multimodal Knowledge-Intensive Benchmark for Short-Video Frame Search in The Gaming Vertical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SVFSearch, the first open benchmark for short-video frame search in the Chinese gaming domain. |
LINGTAO MAO et. al. | arxiv-cs.AI | 2026-05-18 |
| 447 | LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present LogRouter, an end-to-end log question-answering system deployed on TUBITAK BILGEM’s national big data platform that combines a PySpark-based Drain3 ingestion pipeline, GPU-accelerated embeddings, and dual-index storage in Apache Druid and PostgreSQL with pgvector. |
Mert Coskuner; Merve Zeybel; Melik Mert Dolan; | arxiv-cs.LG | 2026-05-18 |
| 448 | Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, constrained by the standard RAG pipeline, these models often rely on curated, static datasets instead of expert-proven biological workflows, lacking the fine-grained information processing and struggling to generalize to novel (OOD) proteins. To bridge this gap, we propose 2D-ProteinRAG, a novel framework that empowers LLMs to operate within the gold-standard biological research workflow (BLAST). |
LI DING et. al. | arxiv-cs.IR | 2026-05-17 |
| 449 | Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascades Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We analyze how automatic speech recognition (ASR) errors propagate through ASR-LLM cascades in Korean spoken question answering (SQA), focusing on downstream semantic failures that conventional ASR metrics cannot fully capture. |
Donghyuk Jung; Youngwon Choi; | arxiv-cs.CL | 2026-05-17 |
| 450 | UCSF-PDGM-VQA: Visual Question Answering Dataset for Brain Tumor MRI Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a clinically relevant visual question answering (VQA) benchmark — the UCSF-PDGM-VQA dataset — consisting of 2,387 QA pairs from 473 glioma-related MRI studies in the public UCSF-PDGM dataset. |
Shiv Ghosh; Junayd Lateef; Yannan Yu; Andreas M. Rauschecker; Madhumita Sushil; | arxiv-cs.CV | 2026-05-16 |
| 451 | DICE: Disentangling Causal Evidence for Multimodal Textbook Question Answering Via Attentive Embedding Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Xu Gu; Yu Li; Bingke Zhu; Jinqiao Wang; Xiaolin Qin; | Pattern Recognition | 2026-05-15 |
| 452 | H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory Via A Hybrid Structure Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus). A few recent works have attempted to exploit agents’memory for improving their … |
Jiawei Yu; Yixiang Fang; Xilin Liu; Yuchi Ma; | arxiv-cs.CL | 2026-05-15 |
| 453 | GRASP: Graph Agentic Search Over Propositions for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Graph Agentic Search over Propositions (GRASP), an agentic system that simultaneously optimizes for high accuracy and minimal token usage in multi-hop question answering. |
Stockton Jenkins; Ramya Korlakai Vinayak; Junjie Hu; | arxiv-cs.MA | 2026-05-15 |
| 454 | Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through comprehensive analysis, we identify two critical limitations in current VLMs: inadequate cross-modal retrieval of structured procedures given visual states, and misalignment between image sequence granularity and textual step decomposition. To address these issues, we present Chain-of-Procedure (CoP), a hierarchical reasoning framework that first retrieves relevant instructions using visual cues, then performs step refinement through semantic decomposition, and finally generates the next step. |
GUANHUA CHEN et. al. | arxiv-cs.CL | 2026-05-14 |
| 455 | FINESSE-Bench: A Hierarchical Benchmark Suite for Financial Domain Knowledge and Technical Analysis in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present FINESSE-Bench, a suite of eight specialized benchmarks comprising 3,993 questions for hierarchical evaluation of financial competencies in LLMs. |
DMITRY STANISHEVSKII et. al. | arxiv-cs.CL | 2026-05-14 |
| 456 | Retrieval Is Cheap, Show Me The Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We observe that multi-hop question answering is a typical form of step-by-step computation, and that this structured process aligns closely with how code-specialized language models are trained to operate. Motivated by this, we introduce \pyrag, a framework that reformulates multi-hop RAG as program synthesis and execution. |
JIASHUO SUN et. al. | arxiv-cs.AI | 2026-05-13 |
| 457 | MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. |
REZARTA ISLAMAJ et. al. | arxiv-cs.CL | 2026-05-12 |
| 458 | Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This limits their effectiveness in single-turn settings, where the user’s latent goal must be inferred from minimal input and integrated into the thinking and reasoning process. To bridge this gap, we propose IAP (Intent-Aware Personalization), a reinforcement learning framework that trains models to infer implicit user intent directly from a single-turn question and incorporate it into thinking steps through a tag-based schema for generating personalized, intent-grounded answers. |
Maryam Amirizaniani; Benjamin Charles Germain Lee; Jevin West; Nicholas Weber; | arxiv-cs.CL | 2026-05-12 |
| 459 | Overview of The MedHopQA Track at BioCreative IX: Track Description, Participation and Evaluation of Systems for Multi-hop Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We developed a novel dataset of 1,000 challenging QA pairs spanning diseases, genes, and chemicals, with particular emphasis on rare diseases. |
REZARTA ISLAMAJ et. al. | arxiv-cs.CL | 2026-05-12 |
| 460 | SEP-LLM: Professional QA in The SEP Domain Using Retrieval-Augmented LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing general-purpose large language models show limitations in this field, mainly in knowledge retrieval accuracy, semantic matching, and legal compliance of generated content. Therefore, there is an urgent need to develop a specialized intelligent QA system tailored for the SEP domain.OBJECTIVES: This paper aims to develop an intelligent QA system for the SEP domain, SEP-LLM, to improve knowledge retrieval, semantic matching, and content compliance, providing high-quality automated answers to SEP-related questions.METHODS: We collected and curated a large set of SEP-related regulations, technical standards, and judicial cases to build a high-quality QA dataset. |
CHENCHEN GUO et. al. | ICST Transactions on Scalable Information Systems | 2026-05-11 |
| 461 | ASTRA-QA: A Benchmark for Abstract Question Answering Over Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this setting is still poorly supported by existing benchmarks and evaluation methods, which often lack stable abstract references or rely on coarse similarity metrics and unstable head-to-head comparisons. To alleviate this issue, we introduce ASTRA-QA, a benchmark for AbSTRAct Question Answering over documents. |
SHU WANG et. al. | arxiv-cs.CL | 2026-05-11 |
| 462 | Intravenous Extravasation Report Generation Using Deep Learning, Generative Artificial Intelligence, and Visual Question Answering Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
ADIREK MUNTHULI et. al. | Scientific Reports | 2026-05-11 |
| 463 | Neural at ArchEHR-QA 2026: One Method Fits All: Unified Prompt Optimization for Clinical QA Over EHRs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present Neural1.5, our method for the ArchEHR-QA 2026 shared task at CL4Health@LREC 2026, which comprises four subtasks: question interpretation, evidence identification, answer generation, and evidence alignment. |
Abrar Majeedi; Viswanatha Reddy Gajjala; Sai Prasanna Teja Reddy Bogireddy; Siddhant Rai; | arxiv-cs.CL | 2026-05-11 |
| 464 | AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user medical presentations. |
ROBIN LINZMAYER et. al. | arxiv-cs.AI | 2026-05-11 |
| 465 | Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We extend the Cost-of-Pass framework proposed by Erol et al. (arXiv:2504.13359) to jointly assess output quality, generation cost, and user interaction cost. |
JAKOB STURM et. al. | arxiv-cs.CL | 2026-05-10 |
| 466 | SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose \Ours, a framework that makes query planning explicit through reusable search skills. |
Jinchao Hu; Meizhi Zhong; Kehai Chen; Min Zhang; | arxiv-cs.AI | 2026-05-09 |
| 467 | A Study on Question Answering in The Ceramics Domain Using Large Language Models with Retrieved Triples and Generated Textual Knowledge Prompts Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Qixian Zhang; Fubao He; Kaihua Hu; Juan Li; | Scientific Reports | 2026-05-09 |
| 468 | Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This uncertainty stems from the lack of systematic evaluation of semantic-spatial reasoning in volumetric medical VLMs for clinically reliable decision support. To address this gap, we introduce CT-SpatialVQA, a benchmark designed to evaluate semantic-spatial reasoning in 3D CT data. |
Mashrafi Monon; Umaima Rahman; Asif Hanif; Numan Saeed; Mohammad Yaqub; | arxiv-cs.CV | 2026-05-09 |
| 469 | A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Open-ended question answering (QA), the most common deployment setting for modern LLMs, is where existing evaluation methods fall short: logit-based metrics need restricted output formats and internal probabilities; verbalized confidence is self-reported and often overconfident; and sampling-based methods rely on task-specific extraction rules without a clear finite-sample target. We introduce Sem-ECE (Semantic-Sampling Expected Calibration Error), a calibration evaluation framework for open-ended QA that samples answers from the model, groups them into semantic classes, and uses the resulting frequencies as confidence. |
ZHANLIANG WANG et. al. | arxiv-cs.CL | 2026-05-08 |
| 470 | Multi-agent Decision Making: A Blackwell’s Informativeness Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we provide a principled approach to analyse decisions made in the multi-LLM setting using Blackwell’s informativeness framework. |
Zheng Zhang; Cuong C. Nguyen; Kevin Wells; Gustavo Carneiro; | arxiv-cs.LG | 2026-05-07 |
| 471 | Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CoExVQA, a self-explainable DocVQA framework with a grounded reasoning process through a chain-of-explanation design. |
Kjetil Indrehus; Adrian Duric; Changkyu Choi; Ali Ramezani-Kebrya; | arxiv-cs.LG | 2026-05-07 |
| 472 | Tatarstan Toponyms: A Bilingual Dataset and Hybrid RAG System for Geospatial Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: This paper addresses automatic geospatial question answering over multilingual toponymic data. An original bilingual dataset of toponyms of the Republic of Tatarstan is … |
Mullosharaf K. Arabov; | arxiv-cs.CL | 2026-05-07 |
| 473 | Evaluation of Large Language Models in Cardiovascular Surgery: A Comparative Study of Board-level Clinical Question Answering and Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Mehmet Inanc Yesilkaya; Uzeyir Yilmaz; | Journal of Cardiothoracic Surgery | 2026-05-07 |
| 474 | Knowledge-Graph Paths As Intermediate Supervision for Self-Evolving Search Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, we observe that constructing and solving a multi-hop question can involve overlapping intermediate entities: the factual bridges used to formulate the question may provide approximate waypoints for answering it. Exploiting this overlap, we introduce Waypoint Coverage Reward (WCR), which grants graded partial credit to incorrect Solver trajectories according to their coverage of entities on the construction path, while preserving full reward for correct answers. |
HUYU WU et. al. | arxiv-cs.AI | 2026-05-07 |
| 475 | Inference-Time Budget Control for LLM Search Agents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Under such dual budgets, better answers require not only stronger models, but also explicit control over which search action should receive the next budget unit and when the accumulated evidence is sufficient to commit a final answer. We study this problem in multi-hop question answering (QA) and formulate it as two-stage inference-time budget control. |
ZHENGRU FANG et. al. | arxiv-cs.AI | 2026-05-07 |
| 476 | VTAgent: Agentic Keyframe Anchoring for Evidence-Aware Video TextVQA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The results suggest that the primary bottleneck lies in the localization of key question-relevant evidence, rather than in reasoning capacity itself. Building on this insight, we propose a question-guided agent framework that explicitly anchors the relevant keyframes before answering. |
Haibin He; Maoyuan Ye; Jing Zhang; Juhua Liu; Bo Du; | arxiv-cs.CV | 2026-05-06 |
| 477 | CANDI: Contextual Alignment for Niche Domains Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a robust baseline, we present MTSS-Net, a lightweight neuro-symbolic framework combining neural retrieval with rule-based reasoning. |
MEGHA CHAKRABORTY et. al. | arxiv-cs.CL | 2026-05-06 |
| 478 | Agentic Retrieval-Augmented Generation for Financial Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FinAgent-RAG, an agentic RAG framework that orchestrates iterative retrieval-reasoning loops with self-verification, specifically engineered for the precision requirements of financial numerical reasoning. |
Yang Shu; Yingmin Liu; Zequn Xie; | arxiv-cs.AI | 2026-05-06 |
| 479 | Temporal Reasoning Is Not The Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel neuro-symbolic question-answering framework governed by a Probabilistic Inconsistency Signal (PIS) that explicitly isolates perceptual errors from reasoning failures. |
Tran Quang Liem; | arxiv-cs.AI | 2026-05-05 |
| 480 | BIT.UA-AAUBS at ArchEHR-QA 2026: Evaluating Open-Source and Proprietary LLMs Via Prompting in Low-Resource QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents the joint participation of the BIT.UA and AAUBS groups in the ArchEHR-QA 2026 shared task, which focuses on clinical question answering and evidence grounding in a low-resource setting. |
RICHARD A. A. JONKER et. al. | arxiv-cs.CL | 2026-05-05 |
| 481 | DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by the Dual Process Theory in cognitive science and Stanovich’s Cognitive Misers Theory, we propose an effective multi-hop QA framework DTKG (Dual-Track Knowledge Graph) through building a two-stage pipeline: i) Classification Stage (dynamic question categorization via few-shot prompting, emulating unconscious processing); and ii) Branch Processing Stage (tailored reasoning paths, emulating conscious processing). |
CHANGHAO WANG et. al. | icml | 2026-05-05 |
| 482 | KARMA-MV: A Benchmark for Causal Question Answering on Music Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a causal knowledge graph (CKG) approach that augments vision-language models (VLMs) with structured retrieval of cross-modal dependencies. |
Archishman Ghosh; Abhinaba Roy; Dorien Herremans; | arxiv-cs.CV | 2026-05-05 |
| 483 | LakeQA: A Benchmark for Complex Exploratory QA Over A Million-Scale Data Lake Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we introduce LakeQA, a comprehensive benchmark for search-centric question answering over data lakes that jointly emphasizes \emph{searching} and \emph{reasoning} capabilities. |
HAONAN WANG et. al. | icml | 2026-05-05 |
| 484 | Question Answering Dataset for Information Retrieval in Slovak Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study introduces a benchmark for evaluating information retrieval in the Slovak language, leveraging a question-answering dataset for fine-tuning and assessment of sentence transformers. |
Daniel Hládek; Michal Stromko; Kristián Sopkovič; Matúš Pleva; Ming-Hsiang Su; | PeerJ Computer Science | 2026-05-05 |
| 485 | Holi-Spatial: Evolving Video Streams Into Holistic 3D Spatial Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose \textbf{Holi-Spatial}, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. |
YUANYUAN GAO et. al. | icml | 2026-05-05 |
| 486 | Video-in-the-Loop: Span-Grounded Long Video QA with Interleaved Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present $\textit{Video-in-the-Loop}$ (ViTL), a two-stage long-video QA framework that preserves a fixed token budget by first $\textit{localizing}$ question-relevant interval(s) with a low-fps skim and then $\textit{answering}$ via span-aware reallocation of visual tokens at higher effective frame rate, emitting an interleaved output with both spans and the final option for direct attribution. |
CHENDONG WANG et. al. | icml | 2026-05-05 |
| 487 | A Linear Expectation Constraint for Selective Prediction and Routing with False-Discovery Control Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose LEC, a principled framework that reframes selective prediction as a decision problem governed by a linear expectation constraint over selection and error indicators. |
ZHIYUAN WANG et. al. | icml | 2026-05-05 |
| 488 | Solving Physics Olympiad Via Reinforcement Learning on Physics Simulators Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we show that physics simulators can serve as a powerful alternative source of supervision for training LLMs for physical reasoning. |
MIHIR PRABHUDESAI et. al. | icml | 2026-05-05 |
| 489 | EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing models are still limited to coarse-grained emotion perception or deficient reasoning capabilities. To bridge this gap, we introduce **EEmoDB**, the largest image-evoked emotion understanding dataset to date. |
LANCHENG GAO et. al. | icml | 2026-05-05 |
| 490 | OpenTSLM: Time-Series Language Models for Reasoning Over Multivariate Medical Text- and Time-Series Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose OpenTSLM, a family of Time Series Language Models that integrate time-series as a native modality into pretrained LLMs, enabling natural-language prompting and reasoning over multiple time-series. |
PATRICK LANGER et. al. | icml | 2026-05-05 |
| 491 | Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Flexi-LoRA, a novel framework that dynamically adjusts LoRA ranks based on input complexity during both training and inference. |
Z. Li; Y. Su; H. Zhou; Z. Fu; N. Collier; | icassp | 2026-05-04 |
| 492 | CE-GOCD: Central Entity-Guided Graph Optimization for Community Detection to Augment LLM Scientific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This impairs the LLM’s comprehension of scientific literature, hindering the comprehensiveness and specificity of its responses. To address this, we propose Central Entity-Guided Graph Optimization for Community Detection (CE-GOCD), a method that augments LLMs’ scientific question answering by explicitly modeling and leveraging semantic substructures within academic knowledge graphs. |
J. Lan; | icassp | 2026-05-04 |
| 493 | Knowledge Editing with Demonstration Selection for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, experiments indicate that existing KE methods for MQA often perform poorly, primarily because the pre-trained LLMs struggle to decompose sub-questions when relying on the same demonstration template. To address this issue, we propose Knowledge Editing with Demonstration Selection for Multi-hop Question Answering (KEDS), which dynamically selects the most suitable demonstrations using both similarity and latent concept modeling for any given MQA query. |
H. Zhao; J. Li; C. Tan; E. Yilmaz; S. Liang; | icassp | 2026-05-04 |
| 494 | Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a multi-domain Audio Question Answering (MD-Audio) benchmark designed to advance research in multi-domain sound understanding. |
C. -H. H. Yang; | icassp | 2026-05-04 |
| 495 | Exploiting Latent and Implicit Chain of Thought for Efficient Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we examine whether implicit and latent CoT methods can be applied effectively to multi-hop QA, focusing on COCONUT (Chain of Continuous Thought) and implicit stepwise CoT internalization. |
H. Shakil; V. Srinivasan; H. Jeelani; K. Gunaratna; S. Chappidi; | icassp | 2026-05-04 |
| 496 | Biomed-R2: Joint Diversity Retrieval and Evidence Reasoning for Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Typical retrieval-augmented generation (RAG) and its variants suffer from insufficient patient alignment, weak evidence integration, and fragile reasoning when dealing with biomedical question answering (QA). To address these challenges, we propose Biomed-R2, a two-stage RAG framework. |
H. GUAN et. al. | icassp | 2026-05-04 |
| 497 | DaPT: A Dual-Path Framework for Multilingual Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG … |
Y. Wang; | icassp | 2026-05-04 |
| 498 | KG2QA: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Communication Standards Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The rapid evolution of communication technologies has led to an explosion of standards, rendering traditional expert-dependent consultation methods inefficient and slow. To address this challenge, we propose KG2QA, a question answering (QA) framework for communication standards that integrates fine-tuned large language models (LLMs) with a domain-specific knowledge graph (KG) via a retrieval-augmented generation (RAG) pipeline. |
Z. Luo; W. Wan; T. Zhang; D. Wang; X. Tang; | icassp | 2026-05-04 |
| 499 | ReTools: Reflection-Enhanced Tool Invocation for Domain-Specific QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing approaches such as ReAct and RestGPT partially mitigate these issues yet remain limited in handling multi-step reliability, iterative recovery, and domain robustness. To address these gaps, we propose ReTools, a Tree-of-Thoughts (ToT) based framework that integrates three modules: (1) task planning, which decomposes complex queries into executable subtasks; (2) tool planning, which selects tools and generates accurate parameters while supporting reflective correction; and (3) reflective iteration, which monitors execution results and adapts to domain-specific requirements. |
F. Dong; | icassp | 2026-05-04 |
| 500 | SEARAG: Semantic Entropy-Guided Adaptive Retrieval for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing adaptive retrieval methods rely on output probabilities or heuristics, which poorly capture the model’s true knowledge needs. To address this, we propose Semantic Entropy-based Adaptive RAG (SEARAG), which trains a discriminative model to predict binary semantic entropy from hidden-layer states, quantifying uncertainty in real time. |
D. Yu; Q. Lin; Z. Yang; L. Zhou; | icassp | 2026-05-04 |
| 501 | M3GQA: A Multimodal Multi-Hop and Knowledge Graph-Based Framework for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) have significantly advanced this field, effectively tackling these complex tasks remains a major challenge. To address this, we introduce M3GQA, a novel framework that constructs a unified knowledge graph from multimodal data and performs multi-hop reasoning over it. |
S. HU et. al. | icassp | 2026-05-04 |
| 502 | TRACE: Optimizing Multi-Hop Question Answering Via Confidence-Guided Retrieval Assimilation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by human cognition, we propose TRACE, which generates candidate answers at cognitive transfer points and evaluates their confidence. |
Y. Han; | icassp | 2026-05-04 |
| 503 | MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose MedSpeak, a novel knowledge graph-aided ASR error correction framework that refines noisy transcripts and improves downstream answer prediction by leveraging both semantic relationships and phonetic information encoded in a medical knowledge graph, together with the reasoning power of LLMs. |
Y. Song; | icassp | 2026-05-04 |
| 504 | SubQRAG: Sub-Question Driven Dynamic Graph Rag Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this broad-view approach often lacks the deep structured reasoning required for complex multi-hop question answering (QA), leading to incomplete evidence and error accumulation. To address these issues, we propose SubQRAG1, a sub-question driven framework that enhances reasoning depth. |
J. Li; | icassp | 2026-05-04 |
| 505 | Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Semantic Reformulation Entropy (SRE), which improves uncertainty estimation in two ways. |
C. TONG et. al. | icassp | 2026-05-04 |
| 506 | Temporal-Aware Heterogeneous Graph Reasoning with Multi-view Fusion for Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. |
W. Wen; | icassp | 2026-05-04 |
| 507 | MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This makes it difficult to assess model robustness in real-world settings. We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise. |
Yingyun Li; Yu Wang; Haiyang Qian; | arxiv-cs.CL | 2026-05-04 |
| 508 | Reliable Database Question Answering with Collaborative Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose RAISR, a master–auxiliary multi-agent framework for robust SQL parsing. |
M. ZHANG et. al. | icassp | 2026-05-04 |
| 509 | Game-Theoretic Insights Into Multi-Agent LLM Debate for Enhanced Clinical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We implement MAD for multiple-choice medical questions (MedQA) and evaluate it across five frontier language models, comparing single-shot prompting, chain-of-thought (CoT), and MAD with 2 or 3 agents. |
S. Sudhakara; S. Sudhakara; | icassp | 2026-05-04 |
| 510 | Enhancing Audio Question-Answering Performance Through Log-Likelihood Guided Reward Functions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a new reward framework for extracting learning signals from multiple-choice questions (MCQs). |
S. Blouir; | icassp | 2026-05-04 |
| 511 | CoreAnchor-QA: Center-Anchored and Self-Improving for Question-Answer Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Long document question-answering (QA) data is crucial for deploying domain models, yet high-quality corpora are scarce and cross-chunk evidence is hard to align, which makes generated questions and answers drift from the main theme. To address this, we propose CoreAnchor-QA, a framework that builds semantic anchors at document and chunk levels, asks from multiple perspectives around a fixed answer, and applies a two-stage gate based on semantic consistency and judgment scores together with a failure memory to form a self-improving loop. |
M. HUANG et. al. | icassp | 2026-05-04 |
| 512 | Pathfinder: MCTS and LLM Feedback-Based Path Selection for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Hence, we propose PATHFINDER, an approach that: (i) uses Monte Carlo Tree Search to generate training path traces, (ii) improves training data quality by filtering erroneous and lengthy traces using sub-answer recall and LLM-as-a-judge verification, and (iii) reformulates sub-queries to handle failed retrieval cases. |
D. P. Maram; K. Gunaratna; V. Srinivasan; H. Jeelani; S. Chappidi; | icassp | 2026-05-04 |
| 513 | Reasoner-Assisted Planning: Enhance The Ability of Graph-Rag to Handle Complex Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recently, multi-step retrieval methods informed by question planning have demonstrated notable improvements in performance; nevertheless, several challenges persist: omitting the crucial conditions of the question and some queries are not appropriate for planning. To mitigate these challenges, we introduce RPlanRAG. |
W. Hou; | icassp | 2026-05-04 |
| 514 | SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the issues, we propose a Schema-aware Cumulative Process Reward Model (SCPRM) that evaluates reasoning paths by conditioning on the reasoning prefix , and incorporating schema distance between current reasoning step and the implicit target parsed from the query, which provides cumulative and future rewards to guide the path explorations. |
Jiujiu Chen; Yazheng Liu; Sihong Xie; Hui Xiong; | arxiv-cs.AI | 2026-05-04 |
| 515 | DocLayout: Elevating The Role of Complex Layout Understanding in Document Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: An empirical evaluation of 12 LVLMs shows that these challenges persist in complex layout understanding. To address this limitation, we introduce the Layout-aware Reasoning and Document Understanding (LRDU) framework, which combines semantic document segmentation and multi-agent collaboration. |
J. Zhang; | icassp | 2026-05-04 |
| 516 | Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on Modern Greek, for which only a limited number of question answering (QA) datasets have been proposed, most of which are intended for model evaluation. |
Nikolaos Giarelis; Charalampos Mastrokostas; Nikos Karacapilidis; | arxiv-cs.CL | 2026-05-03 |
| 517 | Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Flexi-LoRA, a novel framework that dynamically adjusts LoRA ranks based on input complexity during both training and inference. |
Zongqian Li; Yixuan Su; Han Zhou; Zihao Fu; Nigel Collier; | arxiv-cs.LG | 2026-05-03 |
| 518 | Dynamic Knowledge Correction Via Abductive for Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yulin Zhou; Ruizhang Huang; Chuan Lin; Lijuan Liu; Yongbin Qin; | Inf. Process. Manag. | |
| 519 | Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Learning-to-Defer framework that routes EQA queries across a pool of models with varying capabilities and costs to balance accuracy and efficiency. |
Yannis Montreuil; Yeo Shu Heng; Axel Carlier; Lai Xing Ng; Wei Tsang Ooi; | aistats | 2026-05-02 |
| 520 | SF20K Competition 2025: Summary and Findings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Workshop at ICCV 2025. |
Ridouane Ghermi; Xi Wang; Vicky Kalogeiton; Ivan Laptev; | arxiv-cs.CV | 2026-05-02 |
| 521 | Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by active perception theory, which posits that models gain information by acquiring data that differs from their expectations, we introduce Video Active Perception (VAP), a training-free method to enhance long-form video QA using VLMs. |
MARTIN Q. MA et. al. | arxiv-cs.CV | 2026-05-02 |
| 522 | Generative Pre-training for Low-resource Question Answering: Integrating Morphological Constraints in Assamese Language Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
M. Lahkar; Tribikram Pradhan; Utpal Sharma; | Inf. Sci. | 2026-05-01 |
| 523 | SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To effectively reduce token length while preserving essential curve characteristics, we propose a spectral data sampling and interpolation reconstruction approach. |
JIALU SHEN et. al. | arxiv-cs.AI | 2026-04-30 |
| 524 | HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The proposed approach uses a multi-stage cascaded pipeline powered by the Gemini 2.5 Pro large language model to interpret patient-authored questions and retrieve relevant evidence from lengthy clinical notes. |
MD BIPLOB HOSEN et. al. | arxiv-cs.CL | 2026-04-29 |
| 525 | DRAGON: A Benchmark for Evidence-Grounded Visual Reasoning Over Diagrams Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DRAGON, a benchmark for evaluating evidence-grounded visual reasoning in diagrams. |
ANIRUDH IYENGAR KANIYAR NARAYANA IYENGAR et. al. | arxiv-cs.CV | 2026-04-28 |
| 526 | DIAGRAMS: A Review Framework for Reasoning-Level Attribution in Diagram QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present DIAGRAMS, a lightweight, schema-driven review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters. |
ANIRUDH IYENGAR KANIYAR NARAYANA IYENGAR et. al. | arxiv-cs.CL | 2026-04-28 |
| 527 | Efficient and Accurate Medical AI: MediLore and MediOut Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing large language models (LLMs) achieve strong performance but are often limited by high memory usage, latency, and inconsistent behavior in handling rare or complex clinical queries. This study addresses these limitations by exploring efficient and robust modeling strategies for medical QA. |
S. Mohamed Rayhan; M. Hariprasath; K. Hemalatha; | Frontiers in Artificial Intelligence | 2026-04-28 |
| 528 | BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. |
Richard A. A. Jonker; Bárbara Maria Ribeiro de Abreu Martins; Sérgio Matos; | arxiv-cs.CL | 2026-04-28 |
| 529 | CAN-QA: A Question-Answering Benchmark for Reasoning Over In-Vehicle CAN Traffic Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this formulation abstracts away the temporal and relational structure of CAN traffic and misaligns with real-world forensic workflows, which require systematic reasoning about traffic behavior. To address this gap, we introduce CAN-QA, the first benchmark that reformulates CAN traffic analysis as a question-answering (QA) task. |
Jing Chen; Abhijay Deevi; Onat Gungor; Tajana Rosing; | arxiv-cs.CR | 2026-04-27 |
| 530 | Less Is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose CORE, a two-stage sentence-level prompt compression method that eliminates the need for SLMs. |
ZIHUAI XU et. al. | arxiv-cs.CL | 2026-04-27 |
| 531 | Using Hierarchical Syntactic Transformer and Speech Act Identification for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Jui-Feng Yeh; Kuei-Mei Lin; | Multimedia Tools and Applications | 2026-04-27 |
| 532 | An Intelligent Multi-Agent RAG System for Attributed Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Multi-agent systems (MAS) offer an efficient design pattern for tackling complex distributed issues by utilising numerous independent agents that work together towards a shared goal. |
Dr. Bhagyashree Dharaskar; | International Journal for Research in Applied Science and … | 2026-04-25 |
| 533 | Development and Comparative Evaluation of Knowledge Graph–enhanced Large Language Models for Domain-specific Question Answering in Nursing Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
JIYUAN SHI et. al. | BMC Nursing | 2026-04-24 |
| 534 | Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces the task of analytical question answering over large, semi-structured document collections. |
Zhanli Li; Yixuan Cao; Lvzhou Luo; Ping Luo; | arxiv-cs.CL | 2026-04-24 |
| 535 | BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Bayesian Ensemble Retrieval-Augmented Generation (BERAG), along with Bayesian Ensemble Fine-Tuning (BEFT), as a RAG framework in which language models are conditioned on individual retrieved documents rather than a single combined context. |
Jinghong Chen; Jingbiao Mei; Guangyu Yang; Bill Byrne; | arxiv-cs.CL | 2026-04-24 |
| 536 | Encoder-Free Human Motion Understanding Via Structured Motion Descriptions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by biomechanical analysis, where joint angles and body-part kinematics have long served as a precise descriptive language for human movement, we propose \textbf{Structured Motion Description (SMD)}, a rule-based, deterministic approach that converts joint position sequences into structured natural language descriptions of joint angles, body part movements, and global trajectory. |
Yao Zhang; Zhuchenyang Liu; Thomas Ploetz; Yu Xiao; | arxiv-cs.CV | 2026-04-23 |
| 537 | AUDITA: A New Dataset to Audit Humans Vs. AI Skill at Audio QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing audio question answering benchmarks largely emphasize sound event classification or caption-grounded queries, often enabling models to succeed through shortcut strategies, short-duration cues, lexical priors, dataset-specific biases, or even bypassing audio via metadata and captions rather than genuine reasoning Thus, we present AUDITA (Audio Understanding from Diverse Internet Trivia Authors), a large-scale, real-world benchmark to rigorously evaluate audio reasoning beyond surface-level acoustic recognition. |
TASNIM KABIR et. al. | arxiv-cs.CL | 2026-04-23 |
| 538 | Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images. |
Azher Ahmed Efat; Seok Hwan Song; Wallapak Tavanapong; | arxiv-cs.CL | 2026-04-23 |
| 539 | An End-to-End Ukrainian RAG for Local Deployment. Optimized Hybrid Search and Lightweight Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a highly efficient Retrieval-Augmented Generation (RAG) system built specifically for Ukrainian document question answering, which achieved 2nd place in the UNLP 2026 Shared Task. |
Mykola Trokhymovych; Yana Oliinyk; Nazarii Nyzhnyk; | arxiv-cs.CL | 2026-04-23 |
| 540 | HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry-consistent cross-attention with type-specific pointer heads. |
Yuyu Liu; Sarang Rajendra Patil; Mengjia Xu; Tengfei Ma; | arxiv-cs.AI | 2026-04-22 |
| 541 | RespondeoQA: A Benchmark for Bilingual Latin-English Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question-answer pairs. |
Marisa Hudspeth; Patrick J. Burns; Brendan O’Connor; | arxiv-cs.CL | 2026-04-22 |
| 542 | T2S-Metrics: Unified Library for Evaluating SPARQL Queries Generated From Natural Language Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present t2s-metrics, an open-source, extensible, and unified evaluation library designed specifically for SPARQL query comparison and execution-based assessment. |
YOUSOUF TAGHZOUTI et. al. | arxiv-cs.IR | 2026-04-22 |
| 543 | FPSBench: A Benchmark for Video Understanding at High Frame Rates Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Modern video-language models are typically trained on videos downsampled to low frames-per-second (FPS), and the most commonly used evaluation benchmarks are designed for low-FPS input as well. To address this shortcoming, we present FPS-Bench, a large video question-answering benchmark designed to evaluate VLMs’ capabilities to understand video at high-frame rates. |
ROHAN CHOUDHURY et. al. | cvpr | 2026-04-21 |
| 544 | SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SAHM, a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning. |
RANIA ELBADRY et. al. | arxiv-cs.CL | 2026-04-21 |
| 545 | SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real-world kitchen hazards. |
JOSUE TORRES-FONSECA et. al. | arxiv-cs.AI | 2026-04-21 |
| 546 | MSJoE: Jointly Evolving MLLM and Sampler for Efficient Long-Form Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present MLLM-Sampler Joint-Evolution (MiSJoE), a novel framework that **jointly evolves** the MLLM and a lightweight key-frame sampler for efficient long-form video understanding. |
WENHUI TAN et. al. | cvpr | 2026-04-21 |
| 547 | VKG-QA: Visual Knowledge Graph-based Question Answer for Large Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by the advances of Large Multimodal Model to capture higher-order relational structures explicitly novel paradigm of \textit{visualized knowledge representation}, where knowledge graphs are transformed into graphical visualizations that LMMs can directly perceive and reason over. To systematically evaluate this capability, we introduce \textbf{VKG-QA}, a benchmark for \textit{Visual Knowledge Graph-based Question Answering}, covering three major categories and fourteen subtasks. |
YUNTAO DU et. al. | cvpr | 2026-04-21 |
| 548 | MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes MuKV, a method that features a multi-grained KV cache compression module and a semi-hierarchical retrieval approach to improve both efficiency and accuracy for long streaming VideoQA. |
Junbin Xiao; Jiajun Chen; Tianxiang Sun; Xun Yang; Angela Yao; | cvpr | 2026-04-21 |
| 549 | HanDyVQA: A Video QA Benchmark for Fine-Grained Hand-Object Interaction Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce HanDyVQA, a fine-grained video question-answering benchmark that comprehensively covers both the manipulation and effect aspects of HOI. |
Masatoshi Tateno; Gido Kato; Hirokatsu Kataoka; Yoichi Sato; Takuma Yagi; | cvpr | 2026-04-21 |
| 550 | MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and are largely not open-ended, given the difficulty of evaluating free-form answers. In this paper, we introduce a novel open-ended multi-modal VideoQA benchmark, **MovieRecaps** created using movie recap videos—a distinctive type of YouTube content that summarizes a film by presenting its key events through synchronized visual (recap video) and textual (recap summary) modalities. |
Shaden Shaar; Bradon Michael Thymes; Sirawut Chaixanien; Claire Cardie; Bharath Hariharan; | cvpr | 2026-04-21 |
| 551 | Knowing Thyself: Ego-Grounding for Personalized Question-Answering in Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding – the ability to understand the camera-wearer in egocentric videos. |
Junbin Xiao; Shenglang Zhang; Pengxiang Zhu; Angela Yao; | cvpr | 2026-04-21 |
| 552 | An Answer Is Just The Start: Related Insight Generation for Open-Ended Document-Grounded QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present InsightGen, a two-stage approach that first constructs a thematic representation of the document collection using clustering, and then selects related context based on neighborhood selection from the thematic graph to generate diverse and relevant insights using LLMs. |
Saransh Sharma; Pritika Ramu; Aparna Garimella; Koyel Mukherjee; | arxiv-cs.CL | 2026-04-21 |
| 553 | VideoAutoThink: Video Auto Reasoning Via Thinking Once, Answering Twice Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we first demonstrate that for RL-trained video models, direct answering often matches or even surpasses CoT performance, despite CoT producing step-by-step analyses at a higher compute cost. Motivated by this, we propose VideoAutoThink, a video understanding framework that adopts a “reason-when-necessary” strategy. |
SHUMING LIU et. al. | cvpr | 2026-04-21 |
| 554 | VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current benchmarks fail to capture this essential aspect of video understanding. To address this gap, we introduce VRR-QA, a benchmark for Visual Relational Reasoning Beyond Explicit Cues. |
SWETHA SIRNAM et. al. | cvpr | 2026-04-21 |
| 555 | Video-Only ToM: Enhancing Theory of Mind in Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The impact of LLM hallucinations on such tasks is also underexplored from an interpretability perspective. To address these issues, we introduce VisionToM, a vision-oriented intervention framework designed to strengthen task-aware reasoning. |
SIQI LIU et. al. | cvpr | 2026-04-21 |
| 556 | DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This one-size-fits-all” strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or overly lengthy responses to graph-related queries. To address this, we propose the $\mbox{DynamicGTR}$ framework, which dynamically selects the optimal GTR for each query during inference, thereby enhancing the zero-shot graph QA capabilities of VLMs with a customizable accuracy and brevity trade-off. |
YANBIN WEI et. al. | cvpr | 2026-04-21 |
| 557 | Act Like A Pathologist: Tissue-Aware Whole Slide Image Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we try to bring models closer to how humans actually examine slides. |
WENTAO HUANG et. al. | cvpr | 2026-04-21 |
| 558 | Domain-oriented RAG Assessment (DoRA): Synthetic Benchmarking for RAG-based Question Answering on Defense Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present DoRA (Domain-oriented RAG Assessment), a domain-grounded benchmark built from defense documents that pairs synthetic, intent-conditioned QA (question answering) with auditable evidence passages for attribution. |
BAO GIA DOAN et. al. | arxiv-cs.CL | 2026-04-20 |
| 559 | MAEDA: An LLM-Powered Multi-Agent Evaluation Framework for EDA Tool Documentation QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large Language Models (LLMs) have shown remarkable capability in knowledge-intensive scenarios, such as electronic design automation (EDA) tool documentation question answering … |
ZHENGHAO CHEN et. al. | 2026 Design, Automation & Test in Europe Conference (DATE) | 2026-04-20 |
| 560 | Retrieval Augmented Generation Framework for The Nepali Legal Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study presents the first application of a Retrieval Augmented Generation based model for Nepali legal question answering using case laws extracted from the Nepal Kanun Patrika digital archive. |
SAMIR WAGLE et. al. | arxiv-cs.CL | 2026-04-20 |
| 561 | A Hybrid TF-IDF and Knowledge Graph-Enhanced Retrieval-Augmented Generation Framework with Large Language Models for Domain-Aware Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Lilyani Asri Utami; | Journal of Applied Data Sciences | 2026-04-19 |
| 562 | PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Building upon PBSInstr, we develop PBS-VL, a hematopathology-tailored vision-language model for multi-level PBS interpretation at both cell and slide levels. |
YUANLONG WANG et. al. | arxiv-cs.CV | 2026-04-19 |
| 563 | CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, unstable routing can lead to inconsistent expert selection in the same question type, while overly stable routing may reduce flexibility. To address this, we propose Concept-Guided Routing framework (CoGR-MoE), which incorporates semantics of the answer options to guide expert selection in the training phase. |
Xiyin Zeng; Yi Lu; Hao Wang; | arxiv-cs.CV | 2026-04-18 |
| 564 | Toward Auditable Urban Soil Management: A Knowledge Graph and LLM Approach Fusing Environmental and Geochemical Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. |
XI QIN et. al. | Applied Sciences | 2026-04-17 |
| 565 | CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Accordingly, we introduce CoPA, a benchmark with 1,985 user profiles for fine-grained, factor-level assessment. |
HANG SU et. al. | arxiv-cs.CL | 2026-04-16 |
| 566 | DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. |
Neha Srikanth; Jordan Boyd-Graber; Rachel Rudinger; | arxiv-cs.CL | 2026-04-16 |
| 567 | TSQA: Integrating Text Summarization and Question Answering to Improve Information Retrieval from Documents Using Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The aim of this paper is to develop an interaction between TS and QA in three stages to enhance IR performance. |
Ahmed Sami Jaddoa; Jaber Karimpour; Pedram Salehpour; | Information | 2026-04-15 |
| 568 | Leveraging LLM-GNN Integration for Open-World Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present GLOW, a hybrid system that combines a pre-trained GNN and an LLM for open-world KGQA. |
Hussein Abdallah; Ibrahim Abdelaziz; Panos Kalnis; Essam Mansour; | arxiv-cs.CL | 2026-04-15 |
| 569 | Pyramid Graph Neural Network Knowledge Distillation with Pre-trained Language Model for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
XUENING LI et. al. | Engineering Applications of Artificial Intelligence | 2026-04-15 |
| 570 | Understanding The Fundamental Design Decisions of Retrieval-Augmented Generation Systems IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present the first comprehensive study of three universal RAG deployment decisions: whether to deploy RAG, how much information to retrieve, and how to integrate retrieved knowledge effectively. |
SHENGMING ZHAO et. al. | ACM Transactions on Software Engineering and Methodology | 2026-04-15 |
| 571 | Decoding The Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Methodologically, we propose Delta-LLaVA, a novel MLLM framework explicitly tailored for multi-temporal remote sensing interpretation. |
XIAOHE LI et. al. | arxiv-cs.CV | 2026-04-15 |
| 572 | EgoEsportsQA: An Egocentric Video Benchmark for Perception and Reasoning in Esports Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios. To fill this gap, we introduce EgoEsportsQA, a pioneering video question-answering (QA) benchmark for grounding perception and reasoning in expert esports knowledge. |
JIANZHE MA et. al. | arxiv-cs.CV | 2026-04-14 |
| 573 | Calibrated Confidence Estimation for Tabular Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The paper proposes Multi-Format Agreement (MFA), which exploits the lossless and deterministic serialization variation unique to structured data (Markdown, HTML, JSON, CSV) to estimate confidence at 20% lower API cost than sampling baselines. |
Lukas Voss; | arxiv-cs.CL | 2026-04-14 |
| 574 | BoxTuning: Directly Injecting The Object Box for Multimodal Model Fine-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent work addresses this by serializing bounding box coordinates as text tokens, but this text-coordinate paradigm suffers from a fundamental modality mismatch: object information is inherently visual, yet encoding it as text incurs a high token cost that forces aggressive temporal downsampling. We propose BoxTuning, which resolves this mismatch by injecting object spatial-temporal information directly into the visual modality. |
Zekun Qian; Ruize Han; Wei Feng; | arxiv-cs.CV | 2026-04-13 |
| 575 | QFS-Composer: Query-focused Summarization Pipeline for Less Resourced Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a novel QFS framework, QFS-Composer, that integrates query decomposition, question generation (QG), question answering (QA), and abstractive summarization to improve the factual alignment of a summary with user intent. |
Vuk Đuranović; Marko Robnik Šikonja; | arxiv-cs.CL | 2026-04-12 |
| 576 | S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose S3MEM, a structured scene-event episodic memory framework for long-horizon interactive question answering (QA). |
ENCHENG SU et. al. | arxiv-cs.CL | 2026-04-10 |
| 577 | Boosting LLM Performance with Generative Question-answer Pairs Via Wh-transformation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Abstract This study explores methods to enhance the performance of offline Large Language Models (LLMs) using generative question-answer (QA) pairs. |
Wen-Jet Peter Wang; Chen-Sheng Luther Liu; | Concentric. Studies in Linguistics | 2026-04-10 |
| 578 | DeAtt-LMCQA: A Deberta and Attention Based Model of Legal Multi-choice Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Guibin Chen; Xudong Luo; Yanling Li; Binxia Yang; Junlin Zhu; | Artificial Intelligence and Law | 2026-04-09 |
| 579 | Predicting The Benefit of Retrieval Augmentation in Open-Domain Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: While retrieval augmented generation has become a common approach for enhancing question answering systems, retrieval is not universally advantageous. We study the problem of … |
Or Dado; David Carmel; O. Kurland; | 2026-04-09 | |
| 580 | Rag Performance Prediction for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We address the task of predicting the gain of using RAG (retrieval augmented generation) for question answering with respect to not using it. |
Or Dado; David Carmel. Oren Kurland; | arxiv-cs.CL | 2026-04-09 |
| 581 | DTCRS: Dynamic Tree Construction for Recursive Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DTCRS, a method that dynamically generates summary trees based on document structure and query semantics. |
Guanran Luo; Zhongquan Jian; Wentao Qiu; Meihong Wang; Qingqiang Wu; | arxiv-cs.CL | 2026-04-08 |
| 582 | Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe the Yale-DM-Lab system for the ArchEHR-QA 2026 shared task. |
Elyas Irankhah; Samah Fodeh; | arxiv-cs.CL | 2026-04-08 |
| 583 | Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We describe an LLM-assisted pipeline for generating and filtering comparative questions, and benchmark representative audio-language models using both automatic metrics and LLM-as-a-Judge evaluation. |
JUNYOUNG KOH et. al. | arxiv-cs.IR | 2026-04-08 |
| 584 | A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study presents a systematic evaluation of retrieval-augmented medical question answering using the MedQA USMLE benchmark and a structured textbook-based knowledge corpus. |
Nusrat Sultana; Abdullah Muhammad Moosa; Kazi Afzalur Rahman; Sajal Chandra Banik; | arxiv-cs.CL | 2026-04-08 |
| 585 | GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recently proposed CoT-decoding enables the model to generate CoT-style reasoning paths without prompts, but it is only applicable to problems with fixed answer sets. To address this limitation, we propose a general decoding strategy GCoT-decoding that extends applicability to a broader range of question-answering tasks. |
Guanran Luo; Wentao Qiu; Zhongquan Jian; Meihong Wang; Qingqiang Wu; | arxiv-cs.CL | 2026-04-08 |
| 586 | Evaluating Repository-level Software Documentation Via Question Answering and Feature-Driven Development Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) advance documentation generation from code snippets to entire repositories, existing benchmarks have two key limitations: (1) they lack a holistic, repository-level assessment, and (2) they rely on unreliable evaluation strategies, such as LLM-as-a-judge, which suffers from vague criteria and limited repository-level knowledge. To address these issues, we introduce SWD-Bench, a novel benchmark for evaluating repository-level software documentation. |
Xinchen Wang; Ruida Hu; Cuiyun Gao; Pengfei Gao; Chao Peng; | arxiv-cs.SE | 2026-04-08 |
| 587 | Database Querying Under Missing Values Governed By Missingness Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The MG together with the observed DB allow to build a block-independent probabilistic DB, on which basis we propose two QA techniques that jointly capture probabilistic uncertainty and statistical plausibility of the implicit imputation of MVs. |
Leopoldo Bertossi; Farouk Toumani; Maxime Buron; | arxiv-cs.DB | 2026-04-07 |
| 588 | Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. |
HAOPENG LI et. al. | International Journal of Computer Vision | 2026-04-06 |
| 589 | PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering Via Supervised Finetuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study decision-aware query resolution under incomplete information, where a model must determine whether to Answer, Ask for clarification, or Abstain. |
Madhav S Baidya; | arxiv-cs.CL | 2026-04-06 |
| 590 | This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Ideally, LLMs should respond consistently regardless of phrasing, particularly when grounded in the same underlying evidence. We investigate this through a systematic evaluation in a controlled retrieval-augmented generation (RAG) setting for medical question answering (QA), where expert-selected documents are used rather than retrieved automatically. |
HYE SUN YUN et. al. | arxiv-cs.CL | 2026-04-06 |
| 591 | An End-to-End Framework for Building Large Language Models for Software Operations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To explore the potential of LLMs in software operations, we propose OpsLLM, a domain-specific LLM that supports both knowledge-based question answering (QA) and root cause analysis (RCA). |
JINGKAI HE et. al. | arxiv-cs.LG | 2026-04-05 |
| 592 | PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Prefix-based Rollout reuse for Agentic search with Intermediate Step rEwards (PRAISE), a framework for improving both data efficiency and credit assignment in agentic search training. |
ERHAN ZHANG et. al. | arxiv-cs.AI | 2026-04-04 |
| 593 | Document-Level Numerical Reasoning Across Single and Multiple Tables in Financial Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FinLongDocAgent, a Multi-Agent Multi-Round Retrieval-Augmented Generation (RAG) approach that iteratively retrieves evidence, performs intermediate calculations, and verifies results across rounds. |
Yi-Cheng Wang; Wei-An Wang; Chu-Song Chen; | arxiv-cs.CL | 2026-04-04 |
| 594 | V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce V2X-QA, a real-world dataset and benchmark for evaluating MLLMs across vehicle-side, infrastructure-side, and cooperative viewpoints. |
JUNWEI YOU et. al. | arxiv-cs.RO | 2026-04-03 |
| 595 | Injecting Structured Biomedical Knowledge Into Language Models: Continual Pretraining Vs. GraphRAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We then derive a ~100-million-token textual corpus from this graph to continually pretrain two models: BERTUMLS (from BERT) and BioBERTUMLS (from BioBERT). We evaluate these models on six BLURB (Biomedical Language Understanding and Reasoning Benchmark) datasets spanning five task types and evaluate GraphRAG on the two QA (Question Answering) datasets (PubMedQA, BioASQ). |
Jaafer Klila; Sondes Bannour Souihi; Rahma Boujelben; Nasredine Semmar; Lamia Hadrich Belguith; | arxiv-cs.CL | 2026-04-03 |
| 596 | PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: They also do not connect perception with vision-language analysis. To address these limitations, we introduce PaveBench, a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. |
DEXIANG LI et. al. | arxiv-cs.CV | 2026-04-03 |
| 597 | Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we ask a simple question: do we need bigger models for scientific applications? |
Florian Kelber; Matthias Jobst; Yuni Susanti; Michael Färber; | arxiv-cs.IR | 2026-04-02 |
| 598 | Ego-Grounding for Personalized Question-Answering in Egocentric Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding – the ability to understand the camera-wearer in egocentric videos. |
Junbin Xiao; Shenglang Zhang; Pengxiang Zhu; Angela Yao; | arxiv-cs.CV | 2026-04-02 |
| 599 | VideoZeroBench: Probing The Limits of Video MLLMs with Spatio-Temporal Evidence Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current evaluations suffer from two critical limitations: (1) inflated scores can mask deficiencies in fine-grained visual understanding and reasoning, and (2) answer correctness is often measured without verifying whether models identify the precise spatio-temporal evidence supporting their predictions. To address this, we present VideoZeroBench, a hierarchical benchmark designed for challenging long-video question answering that rigorously verifies spatio-temporal evidence. |
JIAHAO MENG et. al. | arxiv-cs.CV | 2026-04-01 |
| 600 | Scalable Conversion of Stack Exchange Network-based Question Answering Sites’ Data Dump to SQL Server Database – A Case of Stack Overflow Data Dump Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Arjumand Fatima; O. Maqbool; | Array | 2026-04-01 |
| 601 | CP-RAG: Mitigating Distracting Content in Retrieval-Augmented Generation for Industrial Knowledge Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: With the increasing adoption of IIoT in industrial production, producing massive heterogeneous data, retrieval-augmented generation (RAG) has become a promising approach for … |
Cong Wang; Shuowen Chai; Tianyi Xu; Muhammad Adil; Tie Qiu; | IEEE Internet of Things Journal | 2026-04-01 |
| 602 | Aqrag: Advanced Quranic Retrieval-augmented Generation for Low-resource Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Mostafa S. A. El-Hakeem; Amirali Abdullah; Sara W. Elhag; Ensaf Hussein Mohamed; | Neural Comput. Appl. | 2026-04-01 |
| 603 | Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we conduct an exploratory analysis of self-reflective reasoning for medical multiple-choice question answering: using GPT-4o and GPT-4o-mini, we compare standard CoT prompting with an iterative self-reflection loop and track how predictions evolve across reflection steps on three widely used medical QA benchmarks (MedQA, HeadQA, and PubMedQA). |
Zaifu Zhan; Mengyuan Cui; Rui Zhang; | arxiv-cs.CL | 2026-03-31 |
| 604 | Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a two-pillar framework, LiteCoST, to achieve both high accuracy and low latency with small language models (SLMs). |
ZHUOWEN LIANG et. al. | arxiv-cs.CL | 2026-03-31 |
| 605 | SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Surgical video question answering is further challenged by low visual contrast, its highly knowledge-driven nature, diverse analytical needs spanning scattered temporal windows, and the hierarchy from basic perception to high-level intraoperative assessment. To address these challenges, we propose SurgTEMP, a multimodal LLM framework featuring (i) a query-guided token selection module that builds hierarchical visual memory (spatial and temporal memory banks) and (ii) a Surgical Competency Progression (SCP) training scheme. |
SHI LI et. al. | arxiv-cs.CV | 2026-03-31 |
| 606 | SeGPruner: Semantic-Geometric Visual Token Pruner for 3D Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose SeGPruner, a semantic-aware and geometry-guided token reduction framework for efficient 3D QA with multi-view images. |
WENLI LI et. al. | arxiv-cs.CV | 2026-03-31 |
| 607 | A Chinese Financial Event Knowledge Graph-based Retrieval-augmented Generation Framework for Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Haitao Cheng; Ke Wang; Qi Wang; Tao Liu; Kai Sheng; | Engineering Applications of Artificial Intelligence | 2026-03-31 |
| 608 | PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a two-stage framework that separates \emph{coverage} from \emph{commitment}. |
XINGYU LI et. al. | arxiv-cs.AI | 2026-03-30 |
| 609 | When Choices Become Priors: Contrastive Decoding for Scientific Figure Multiple-Choice QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose SCICON, a training-free decoding method that scores each candidate by subtracting a text-only option score from its image-conditioned counterpart. |
Taeyun Roh; Eun-yeong Jo; Wonjune Jang; Jaewoo Kang; | arxiv-cs.AI | 2026-03-30 |
| 610 | CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To evaluate it, we introduce \textbf{CDH-Bench}, a benchmark designed to create explicit \textbf{visual evidence–commonsense conflicts}. |
Kesheng Chen; Yamin Hu; Qi Zhou; Zhenqian Zhu; Wenjian Luo; | arxiv-cs.CV | 2026-03-29 |
| 611 | Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce StackRepoQA, the first multi-project, repository-level question answering dataset constructed from 1,318 real developer questions and accepted answers across 134 open-source Java projects. |
Yoseph Berhanu Alebachew; Hunter Leary; Swanand Vaishampayan; Chris Brown; | arxiv-cs.SE | 2026-03-27 |
| 612 | Do Hallucination Neurons Generalize? Evidence from Cross-Domain Transfer in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We ask a natural follow-up question: do H-neurons generalize across knowledge domains? Using a systematic cross-domain transfer protocol across 6 domains (general QA, legal, financial, science, moral reasoning, and code vulnerability) and 5 open-weight models (3B to 8B parameters), we find they do not. |
Snehit Vaddi; Pujith Vaddi; | arxiv-cs.CL | 2026-03-26 |
| 613 | QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. |
Mohammad AL-Smadi; | arxiv-cs.CL | 2026-03-26 |
| 614 | LLaVA-LE: Large Language-and-Vision Assistant for Lunar Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce LLaVA-LE (Large Language-and-Vision Assistant for Lunar Exploration), a vision-language model specialized for lunar surface and subsurface characterization. |
Gokce Inal; Pouyan Navard; Alper Yilmaz; | arxiv-cs.CV | 2026-03-25 |
| 615 | Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. |
Saahil Mathur; Ryan David Rittner; Vedant Ajit Thakur; Daniel Stuart Schiff; Tunazzina Islam; | arxiv-cs.CL | 2026-03-25 |
| 616 | See, Remember, Explore: A Benchmark and Baselines for Streaming Spatial Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Spatial understanding is fundamental for embodied agents, yet most spatial VLMs and benchmarks remain offline-evaluating post-hoc QA over pre-recorded inputs and overlooking two crucial deployment-critical requirements: long-horizon streaming inference and active perception when the current view is insufficient. To address this gap, we introduce S3-Bench, a benchmark suite for streaming spatial question answering with active exploration, where queries are temporally grounded to specific timestamps and must be answered using only observations available up to that moment. |
Yuxi Wei; Wei Huang; Qirui Chen; Lu Hou; Xiaojuan Qi; | arxiv-cs.CV | 2026-03-24 |
| 617 | Mixture of Demonstrations for Textual Graph Understanding and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose MixDemo, a novel GraphRAG framework enhanced with a Mixture-of-Experts (MoE) mechanism for selecting the most informative demonstrations under diverse question contexts. |
Yukun Wu; Lihui Liu; | arxiv-cs.IR | 2026-03-23 |
| 618 | Efficient Fine-Tuning Methods for Portuguese Question Answering: A Comparative Study of PEFT on BERTimbau and Exploratory Evaluation of Generative LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work presents a systematic evaluation of Parameter-Efficient Fine-Tuning (PEFT) and quantization techniques applied to BERTimbau for Question Answering on SQuAD-BR, the Brazilian Portuguese translation of SQuAD v1. |
Mariela M. Nina; Caio Veloso Costa; Lilian Berton; Didier A. Vega-Oliveros; | arxiv-cs.CL | 2026-03-22 |
| 619 | KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving Based on Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present KLDrive, the first knowledge-graph-augmented LLM reasoning framework for fine-grained question answering in autonomous driving. |
YE TIAN et. al. | arxiv-cs.AI | 2026-03-21 |
| 620 | PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present PAVE (Premise-Grounded Answer Validation and Editing), an inference-time validation layer for evidence-grounded question answering. |
Tianyi Huang; Caden Yang; Emily Yin; Eric Wang; Michael Zhang; | arxiv-cs.CL | 2026-03-21 |
| 621 | HORNet: Task-Guided Frame Selection for Video Question Answering with Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce \textbf{HORNet}, a lightweight frame selection policy trained with Group Relative Policy Optimization (GRPO) to learn which frames a frozen VLM needs to answer questions correctly. |
Xiangyu Bai; Bishoy Galoaa; Sarah Ostadabbas; | arxiv-cs.CV | 2026-03-19 |
| 622 | Bypassing Document Ingestion: An MCP Approach to Financial Q&A Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We study whether, and under which conditions, Model Context Protocol (MCP) offers a more reliable alternative to standard retrieval-augmented generation (RAG) by allowing large language models (LLMs) to interact directly with data rather than relying on document ingestion and chunk retrieval. |
Sasan Mansouri; Edoardo Pilla; Mark Wahrenburg; Fabian Woebbeking; | arxiv-cs.IR | 2026-03-19 |
| 623 | FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Administration (FDA) drug label documents. |
BETTY XIONG et. al. | arxiv-cs.CL | 2026-03-19 |
| 624 | DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG … |
YILIN WANG et. al. | arxiv-cs.CL | 2026-03-19 |
| 625 | Multi-layer Biaffine Model and Transformer Question Answering System for Tamil Language Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
S Jeevit Davidson; M Murali; | Engineering Applications of Artificial Intelligence | 2026-03-18 |
| 626 | Halo: Domain-Aware Query Optimization for Long-Context Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Halo, a long-context QA framework that automatically extracts domain knowledge from user prompts and applies it as executable operators across a multi-stage query execution pipeline. |
Pramod Chunduri; Francisco Romero; Ali Payani; Kexin Rong; Joy Arulraj; | arxiv-cs.DB | 2026-03-18 |
| 627 | Attention-guided Evidence Grounding for Spoken Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce Attention-guided Evidence Grounding (AEG), a novel end-to-end framework that leverages the internal cross-modal attention of Speech Large Language Models (SpeechLLMs) to explicitly locate and ground key evidence in the model’s latent space. |
KE YANG et. al. | arxiv-cs.CL | 2026-03-17 |
| 628 | IndexRAG: Bridging Facts for Cross-Document Reasoning at Index Time Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present IndexRAG, a novel approach that shifts cross-document reasoning from online inference to offline indexing. |
Zhenghua Bao; Yi Shi; | arxiv-cs.CL | 2026-03-17 |
| 629 | A Comprehensive Survey on Table Question Answering: Datasets, Methods and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Weiqiang Xu; Yang Liu; Lingfeng Lu; Huakang Li; Guozi Sun; | Engineering Applications of Artificial Intelligence | 2026-03-16 |
| 630 | Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a question-aware keyframe selection framework with two components: pseudo keyframe labels derived from LMMs that provide informative supervision and a coverage regularization that promotes diverse, complementary evidence across time. |
Minchan Kwon; Hyounguk Shon; Junmo Kim; | arxiv-cs.CV | 2026-03-16 |
| 631 | Information Asymmetry Across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We manually construct a novel challenge question-answering (QA) dataset that captures knowledge conveyed on a local Wikipedia page, which is absent from their higher-resource counterparts-covering Mandarin Chinese vs. Cantonese and German vs. Bavarian. |
Renhao Pei; Siyao Peng; Verena Blaschke; Robert Litschko; Barbara Plank; | arxiv-cs.CL | 2026-03-15 |
| 632 | LongVidSearch: An Agentic Benchmark for Multi-hop Evidence Retrieval Planning in Long Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing long-video benchmarks are largely static: they rarely enforce strict multi-hop retrieval and typically lack a standardized evidence-access interface, making it difficult to separate failures in retrieval planning from those in answer generation. To address this gap, we introduce LongVidSearch, a benchmark for evaluating agentic multi-hop evidence retrieval planning in long videos under standardized access constraints. |
Rongyi Yu; Chenyuan Duan; Wentao Zhang; | arxiv-cs.CV | 2026-03-15 |
| 633 | English Visual Question Answering: Building A Culturally Relevant Dataset from Image Captions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes an efficient and scalable VQA system using a pretrained CLIP (Contrastive Language–Image Pretraining) ViT-B/32 model for open-ended English language queries. |
Dr. Md Sirajul Huque; B. Dinesh kumar; Bhoomika Mandadi; C. Anil kumar; | International Journal of Scientific Research in Engineering … | 2026-03-15 |
| 634 | Automatic Inter-document Multi-hop Scientific QA Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present AIM-SciQA, an automated framework for generating multi-document, multi-hop scientific QA datasets. |
Seungmin Lee; Dongha Kim; Yuni Jeon; Junyoung Koh; Min Song; | arxiv-cs.CL | 2026-03-15 |
| 635 | The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose two augmentations: (i) SPARQL chain-of-thought prompting, which decomposes questions into triple-pattern queries aligned with the entity-relationship context, and (ii) graph-walk compression, which compresses the context by ~60% via knowledge-graph traversal with no LLM calls. |
Yasaman Zarinkia; Venkatesh Srinivasan; Alex Thomo; | arxiv-cs.IR | 2026-03-14 |
| 636 | Empowering LLMs with Symbolic Representation and Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous … |
Fengxiang Cheng; | AAAI Conference on Artificial Intelligence | 2026-03-14 |
| 637 | Fine-Tuning Sample Order Matters in Propositional Logical Question-Answering (Student Abstract) Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language models (LLMs) have achieved impressive progress in natural language processing tasks but still struggle with complex logical reasoning. We observe that in … |
Fengxiang Cheng; Chuan-Cang Zhou; Fenrong Liu; R. Rooij; | AAAI Conference on Artificial Intelligence | 2026-03-14 |
| 638 | Sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on A Single Notebook Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. |
Ibrahim Ebrar Yurt; Fabian Karl; Tejaswi Choppa; Florian Matthes; | arxiv-cs.CL | 2026-03-14 |
| 639 | Consistency-Guided Decoding with Proof-Driven Disambiguation for Three-Way Logical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present CGD-PD, a lightweight test-time layer that (a) queries a single 3-way classifier on both $H$ and a mechanically negated form of $H$, (b) projects the pair onto a negation-consistent decision when possible, and (c) invokes a proof-driven disambiguation step that uses targeted binary entailment probes to selectively resolve $Unknown$ outcomes, requiring only an average of 4-5 model calls. |
Tianyi Huang; Ming Hou; Jiaheng Su; Yutong Zhang; Ziling Zhang; | arxiv-cs.CL | 2026-03-12 |
| 640 | FinReflectKG – HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: As organizations increasingly integrate AI-powered question-answering systems into financial information systems for compliance, risk assessment, and decision support, ensuring … |
Mahesh Kumar; Bhaskarjit Sarmah; Stefano Pasquali; | ArXiv | 2026-03-11 |
| 641 | FinReflectKG — HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce FinBench-QA-Hallucination, a benchmark for evaluating hallucination detection methods in KG-augmented financial QA over SEC 10-K filings. |
Mahesh Kumar; Bhaskarjit Sarmah; Stefano Pasquali; | arxiv-cs.CL | 2026-03-11 |
| 642 | ThReadMed-QA: A Multi-Turn Medical Dialogue Benchmark from Real Patient Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ThReadMed-QA, a benchmark of 2,437 fully-answered patient-physician conversation threads extracted from r/AskDocs, comprising 8,204 question-answer pairs across up to 9 turns. |
Monica Munnangi; Saiph Savage; | arxiv-cs.CL | 2026-03-11 |
| 643 | MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a domain-agnostic, KG-based QA framework that covers both the indexing and retrieval/inference phases. |
Riccardo Campi; Nicolò Oreste Pinciroli Vago; Mathyas Giudici; Marco Brambilla; Piero Fraternali; | arxiv-cs.CL | 2026-03-11 |
| 644 | TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The optimization is often unstable due to sparse rewards and difficult credit assignments across reasoning and tool calls. To address this, we introduce Turn-Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn-level rewards to each reasoning + tool-call segment based on the increased likelihood of the correct answer under a teacher model. |
YUTAO XIE et. al. | arxiv-cs.CL | 2026-03-11 |
| 645 | LLM As A Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose \textit{LLM as a Meta-Judge}, a scalable framework that utilizes LLMs to generate synthetic evaluation datasets via controlled semantic degradation of real data, replacing human judgment. |
Lukáš Eigler; Jindřich Libovický; David Hurych; | arxiv-cs.CL | 2026-03-10 |
| 646 | A GraphRAG-Based Question-Answering System for Explainable and Advanced Reasoning Over Air Quality Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The present work introduces an integrated GraphRAG-based Question Answering (QA) system that couples a domain-specific knowledge graph encoding fundamental IAQ concepts and relationships with a RAG-based natural language interface, thereby enabling explainable, context-aware, and advanced analytical reasoning over IAQ data. |
Christos Mountzouris; Grigorios Protopsaltis; John Gialelis; | Air | 2026-03-10 |
| 647 | Emotion Is Not Just A Label: Latent Emotional Factors in LLM Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We analyze how emotional tone systematically alters attention geometry in transformer models, showing that metrics such as locality, center-of-mass distance, and entropy vary across emotions and correlate with downstream question-answering performance. To facilitate controlled study of these effects, we introduce Affect-Uniform ReAding QA (AURA-QA), a question-answering dataset with emotionally balanced, human-authored context passages. |
Benjamin Reichman; Adar Avasian; Samuel Webster; Larry Heck; | arxiv-cs.CL | 2026-03-10 |
| 648 | SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SPD-RAG, a hierarchical multi-agent framework for exhaustive cross-document question answering that decomposes the problem along the document axis. |
Yagiz Can Akay; Muhammed Yusuf Kartal; Esra Alparslan; Faruk Ortakoyluoglu; Arda Akpinar; | arxiv-cs.CL | 2026-03-09 |
| 649 | MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial … |
XINQI FAN et. al. | arxiv-cs.CV | 2026-03-09 |
| 650 | Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). |
FENG LUO et. al. | arxiv-cs.DB | 2026-03-09 |
| 651 | BRIDGE: Benchmark for Multi-hop Reasoning In Long Multimodal Documents with Grounded Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce BRIDGE, a benchmark for multi-hop reasoning over long scientific papers that require integrating evidence across text, tables, and figures. |
Biao Xiang; Soyeon Caren Han; Yihao Ding; | arxiv-cs.CL | 2026-03-08 |
| 652 | MAviS: A Multimodal Conversational Assistant For Avian Species Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing multimodal large language models face challenges when it comes to specialized topics like avian species, making it harder to provide accurate and contextually relevant information in these areas. To address this limitation, we introduce the MAviS-Dataset, a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species, comprising both pretraining and instruction-tuning subsets enriched with structured question-answer pairs. |
YEVHENIIA KRYKLYVETS et. al. | arxiv-cs.CV | 2026-03-07 |
| 653 | RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: They are also only validated in limited settings, leaving it unclear how reliably they handle the shifts encountered in real-world settings. To address these limitations, we introduce RAMoEA-QA, a hierarchically routed generative model for respiratory audio question answering that unifies multiple question types and supports both discrete and continuous targets within a single multimodal system. |
Gaia A. Bertolino; Yuwei Zhang; Tong Xia; Domenico Talia; Cecilia Mascolo; | arxiv-cs.SD | 2026-03-06 |
| 654 | Helpful or Harmful? Re-Evaluating Frugality in Retrieval-Augmented Generation for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work introduces a frugality-based evaluation framework that jointly assesses accuracy improvements and computational cost to determine when retrieval-augmented generation is beneficial in medical question answering, rather than evaluating retrieval effectiveness through accuracy alone. |
Richard Coric; Ebenezer F. Oloyede; Heriberto Cuayáhuitl; | Machine Learning and Knowledge Extraction | 2026-03-06 |
| 655 | NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These systems tend to produce unreliable responses when correct answers are absent from context. To solve this problem, we introduce NCTB-QA, a large-scale Bangla question answering dataset comprising 87,805 question-answer pairs extracted from 50 textbooks published by Bangladesh’s National Curriculum and Textbook Board. |
Abrar Eyasir; Tahsin Ahmed; Muhammad Ibrahim; | arxiv-cs.CL | 2026-03-05 |
| 656 | Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CoCA(Co-optimized Confidence and Answers), a GRPO reinforcement learning framework that jointly optimizes confidence calibration and answer accuracy via segmented credit assignment. |
CHANGCHENG LI et. al. | arxiv-cs.CL | 2026-03-05 |
| 657 | Who Judges The Judge? Evaluating LLM-as-a-Judge for French Medical Open-ended QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluate whether large language models (LLMs) can act as judges of semantic equivalence in French medical OEQA, comparing closed-access, general-purpose, and biomedical domain-adapted models. |
Ikram Belmadani; Oumaima El Khettari; Pacôme Constant dit Beaufils; Richard Dufour; Benoit Favre; | arxiv-cs.CL | 2026-03-04 |
| 658 | RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These approaches fail to diagnose whether an error stems from faulty retrieval or flawed generation, limiting developers from performing targeted improvement. To address this gap, we propose RAG-X, a diagnostic framework that evaluates the retriever and generator independently across a triad of QA tasks: information extraction, short-answer generation, and multiple-choice question (MCQ) answering. |
Aswini Sivakumar; Vijayan Sugumaran; Yao Qiang; | arxiv-cs.CL | 2026-03-03 |
| 659 | KGLMQA: Enhancing Medical Visual Question Answering with Knowledge Graphs and LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing models frequently encounter challenges such as restricted multimodal interaction, insufficient guidance from external medical knowledge, and a lack of rigorous diagnostic logic in their responses. To address these issues, we propose KGLMQA, a novel framework that integrates knowledge graphs with Large Language Models (LLMs). |
Wenhu Wang; Huina Liu; Changfa Wei; | PeerJ Computer Science | 2026-03-03 |
| 660 | Let The Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We show that simply granting an off-the-shelf LLM autonomy, that is, letting it decide what to do next, already yields substantial gains even in a strict zero-shot setting. Building on this insight, we propose AT2QA, an autonomous, training-free agent for temporal question answering that iteratively interacts with the temporal knowledge graph via a general search tool for dynamic retrieval. |
Xufei Lv; Jiahui Yang; Yifu Gao; Linbo Qiao; Houde Liu; | arxiv-cs.CL | 2026-03-02 |
| 661 | Co-MedGraphRAG: A Collaborative Large-Small Model Medical Question-Answering Framework Enhanced By Knowledge Graph Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language models (LLMs) have demonstrated significant capabilities in natural language processing (NLP), but they often encounter challenges in the medical domain. This can … |
Sizhe Chen; Tao Chen; | Inf. | 2026-03-02 |
| 662 | A Self-Reflection Mechanism for Reducing Hallucination in Vietnamese Legal Question Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a self-reflection mechanism that adds an iterative generate–evaluate–refine loop to a Graph-RAG pipeline for Vietnamese labor-law questions. |
THI VUONG PHAM et. al. | Scientific Journal of Computer Science | 2026-03-02 |
| 663 | Evaluating Prompting Strategies for Chart Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a systematic evaluation of four widely used prompting paradigms (Zero-Shot, Few-Shot, Zero-Shot Chain-of-Thought, and Few-Shot Chain-of-Thought) across GPT-3.5, GPT-4, and GPT-4o on the ChartQA dataset. |
Ruthuparna Naikar; Ying Zhu; | arxiv-cs.CL | 2026-03-02 |
| 664 | Refined Generation-based Framework for Consistent and Reliable Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
QUANXING XU et. al. | Pattern Recognit. | 2026-03-01 |
| 665 | Progressive Multi-hop Reasoning for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yuan Ren; Zhijie Nie; Richong Zhang; Xudong Liu; | Inf. Syst. | 2026-03-01 |
| 666 | Bidirectional Causal Learning for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Fa-Ning Long; Peiyi Wei; Peiyu Li; Ming Jiang; Wei Li; | Image Vis. Comput. | 2026-03-01 |
| 667 | Spatial–Temporal Clue Reasoning Chain for Long Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
HAIBO GONG et. al. | IEEE Transactions on Circuits and Systems for Video … | 2026-03-01 |
| 668 | EviMMQA: Multimodal Question Answering for Medical Evidence Extraction in Systematic Reviews Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
CHANGKAI JI et. al. | Pattern Recognit. | 2026-03-01 |
| 669 | Prompt Sensitivity and Answer Consistency of Small Open-Source Large Language Models on Clinical Question Answering: Implications for Low-Resource Healthcare Deployment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We evaluated five open-source models (Gemma 2 2B, Phi-3 Mini 3.8B, Llama 3.2 3B, Mistral 7B, and Meditron-7B domain-pretrained without instruction tuning) across three clinical QA datasets (MedQA, MedMCQA, PubMedQA) using five prompt styles (original, formal, simplified, roleplay, direct). |
Shravani Hariprasad; | arxiv-cs.CL | 2026-02-28 |
| 670 | SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA Over Text and Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SPARTA, an end-to-end construction framework that automatically generates large-scale Table-Text QA benchmarks with lightweight human validation, requiring only one quarter of the annotation time of HybridQA. |
Sungho Park; Jueun Kim; Wook-Shin Han; | arxiv-cs.CL | 2026-02-26 |
| 671 | FHIRPath-QA: Executable Question Answering Over FHIR Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce FHIRPath-QA, the first open dataset and benchmark for patient-specific QA that includes open-standard FHIRPath queries over real-world clinical data. |
Michael Frew; Nishit Bheda; Bryan Tripp; | arxiv-cs.CL | 2026-02-26 |
| 672 | A Novel Multi-modal Attentional Collaborative Learning Framework with Semantic Enhancement for Audio–visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
JIE YANG et. al. | Engineering Applications of Artificial Intelligence | 2026-02-25 |
| 673 | LiCQA : A Lightweight Complex Question Answering System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present LiCQA, an unsupervised question answer- ing model that works primarily on the basis of corpus evidence. |
Sourav Saha; Dwaipayan Roy; Mandar Mitra; | arxiv-cs.CL | 2026-02-25 |
| 674 | Exploring Multimodal LMMs for Online Episodic Memory Question Answering on The Edge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate the feasibility of using Multimodal Large Language Models (MLLMs) for real-time online episodic memory question answering. |
Giuseppe Lando; Rosario Forte; Antonino Furnari; | arxiv-cs.CV | 2026-02-25 |
| 675 | A Dataset for Addressing Patient’s Information Needs Related to Clinical Course of Hospitalization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, robust datasets to assess the factuality and relevance of AI-generated responses are lacking and, to our knowledge, none capture patient information needs in the context of their EHRs. To address this gap, we introduce ArchEHR-QA, an expert-annotated dataset of 134 cases from intensive care unit and emergency department settings. |
Sarvesh Soni; Dina Demner-Fushman; | Scientific Data | 2026-02-25 |
| 676 | Spelling Correction in Healthcare Query-Answer Systems: Methods, Retrieval Impact, and Empirical Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Healthcare question-answering (QA) systems face a persistent challenge: users submit queries with spelling errors at rates substantially higher than those found in the professional documents they search. |
Saurabh K Singh; | arxiv-cs.CL | 2026-02-24 |
| 677 | Controllable Evidence Selection in Retrieval-Augmented Question Answering Via Deterministic Utility Gating Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents a deterministic evidence selection framework for retrieval-augmented question answering. |
Victor P. Unda; | arxiv-cs.CL | 2026-02-23 |
| 678 | Retrieval-Augmented Generation for Multi-Hop Question Answering Based on Structured Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As the number of retrieval iterations increases, the generated queries can gradually drift from the correct reasoning path, and irrelevant or noisy information may accumulate, ultimately reducing reasoning accuracy. To address these challenges, we propose a novel retrieval-augmented generation method for multi-hop question answering based on structured planning. |
Yujiao Huang; Ling Yang; Xu-Hua Yang; Xinli Xu; | ACM Transactions on Knowledge Discovery from Data | 2026-02-23 |
| 679 | To Reason or Not To: Selective Chain-of-Thought in Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed. |
ZAIFU ZHAN et. al. | arxiv-cs.CL | 2026-02-23 |
| 680 | Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. |
WUZHENGHONG WEN et. al. | arxiv-cs.CL | 2026-02-23 |
| 681 | Efficient Multimodal Learning Using BERT and Vision Transformers for Visual Question Answering on Peripheral Blood Cells Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Faheem Shehzad; Ciro Mennella; Massimo Esposito; Aniello Minutolo; | Discover Artificial Intelligence | 2026-02-22 |
| 682 | A Large-scale Benchmark for Evaluating Large Language Models on Medical Question Answering in Romanian Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
ANA-CRISTINA ROGOZ et. al. | npj Digital Medicine | 2026-02-21 |
| 683 | Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Across methods, gains in document discovery tend to translate into stronger page recall, yet oracle performance still suggests headroom for page and chunk level retrieval. To target this gap, we introduce a domain fine-tuned page scorer that treats pages as an intermediate retrieval unit between documents and chunks. |
Amine Kobeissi; Philippe Langlais; | arxiv-cs.CL | 2026-02-19 |
| 684 | InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save |
LAI WEI et. al. | Information Processing & Management | 2026-02-19 |
| 685 | Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We conduct a systematic study of long-context code question answering using controlled ablations that test sensitivity to answer format, distractors, and context scale. |
Kishan Maharaj; Nandakishore Menon; Ashita Saxena; Srikanth Tamilselvam; | arxiv-cs.SE | 2026-02-19 |
| 686 | Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we address this research gap in Greek QA by contributing: (i) DemosQA, a novel dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist; (ii) a memory-efficient LLM evaluation framework adaptable to diverse QA datasets and languages; and (iii) an extensive evaluation of 11 monolingual and multilingual LLMs on 6 human-curated Greek QA datasets using 3 different prompting strategies. |
Charalampos Mastrokostas; Nikolaos Giarelis; Nikos Karacapilidis; | arxiv-cs.CL | 2026-02-18 |
| 687 | MoL: Adaptive Mixture-of-Length Reasoning for Efficient Question Answering with Context Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Mixture-of-Length (MoL), an approach for Question Answering (QA) with context that aims to improve the balance between reasoning quality and response efficiency. |
GUOCONG LI et. al. | iclr | 2026-02-17 |
| 688 | A Structured, Tagged, and Localized Visual Question Answering Dataset with Full Sentence Answers and Scene Graphs for Chest X-ray Images Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these limitations, we introduce MIMIC-Ext-CXR-QBA (abbr.We automatically generated our VQA dataset from scene graphs (also made available), which we constructed using LLM-based information extraction from radiology reports. |
Philip Müller; Friederike Jungmann; Georgios Kaissis; Daniel Rueckert; | iclr | 2026-02-17 |
| 689 | M4PQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose M4PQA, a human-annotated comprehensive paper QA dataset in the field of artificial intelligence, with 13,948 papers and 1,246 questions, that encompasses multi-task, multi-modal and instance-level evaluation. |
TIANCHENG HUANG et. al. | iclr | 2026-02-17 |
| 690 | Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our results reveal a notable performance gap between human level scores and VLM performance, highlighting that current VLMs still fall short of human level spatial understanding (SU). To bridge this gap, we propose Ego3D-VLM, a post-training framework that enhances 3D spatial reasoning of VLMs. |
MOHSEN GHOLAMI et. al. | iclr | 2026-02-17 |
| 691 | QuRL: Rubrics As Judge For Open-Ended Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these limitations, we introduce a schema for generating case-wise rubrics that are question-specific, content-based and stylistically sensitive, thereby evaluating both factual soundness and writing quality. Building on this schema, we propose QuRL (Open-Ended QA with Rubric-guided Reinforcement Learning), a framework that automatically mines rubrics for each question from easily accessible online sources and leverages them as reward signals. |
Xiyu Wei; Qingwei Zong; Xiaoguang Li; Eugene J. Yu; Sujian Li; | iclr | 2026-02-17 |
| 692 | SmartChunk Retrieval: Query-Aware Chunk Compression with Planning for Efficient Document RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SmartChunk retrieval, a query-adaptive framework for efficient and robust long-document question answering (QA). |
Xuechen Zhang; Koustava Goswami; Samet Oymak; Jiasi Chen; Nedim Lipka; | iclr | 2026-02-17 |
| 693 | FrugalRAG: Less Is More in RL Finetuning for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FrugalRAG, a two-stage finetuning framework that adaptively _reduces_ the number of retrieval steps based on a question’s difficulty. |
Abhinav Java; Srivathsan Koundinyan; Nagarajan Natarajan; Amit Sharma; | iclr | 2026-02-17 |
| 694 | A$^2$Search: Ambiguity-Aware Question Answering with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present A$^2$Search, an annotation-free, end-to-end training framework to recognize and handle ambiguity. |
FENGJI ZHANG et. al. | iclr | 2026-02-17 |
| 695 | VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Naively combining reward signals from these tasks results in mutual performance degradation, which we attribute to a conflict between their opposing task natures. To address this challenge, we propose a novel training framework built upon two intermediate proxy tasks: DarkEventInfer, which presents videos with masked event segments, requiring models to infer the obscured content based on contextual video cues; and MixVidQA, which presents interleaved video sequences composed of two distinct clips, challenging models to isolate and reason about one while disregarding the other. |
XINLONG CHEN et. al. | iclr | 2026-02-17 |
| 696 | EgoNight: Towards Egocentric Vision Understanding at Night with A Challenging Benchmark IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Most existing benchmarks for egocentric vision understanding focus primarily on daytime scenarios, overlooking the low-light conditions that are inevitable in real-world applications. To investigate this gap, we present EgoNight, the first comprehensive benchmark for nighttime egocentric vision, with visual question answering (VQA) as the core task. |
DEHENG ZHANG et. al. | iclr | 2026-02-17 |
| 697 | EarthSE: A Benchmark Evaluating Earth Scientific Exploration Capability for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a comprehensive and professional benchmark for the Earth sciences, designed to evaluate the capabilities of LLMs in scientific exploration within this domain, spanning from fundamental to advanced levels.Leveraging a corpus of 100,000 research papers, we first construct two Question Answering (QA) datasets: Earth-Iron, which offers extensive question coverage for broad assessment, and Earth-Silver, which features a higher level of difficulty to evaluate professional depth. |
WANGHAN XU et. al. | iclr | 2026-02-17 |
| 698 | Are LLMs Really Not Knowledgeable? Mining The Submerged Knowledge in LLMs’ Memory Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By analyzing the token-level output distributions, we find that correct answers often appear among high-probability candidates, despite not being selected. Motivated by this, we propose Hits@k, a novel metric to evaluate latent knowledge retention independent of answer surface form. |
Xingjian Tao; Yiwei Wang; Yujun Cai; Zhicheng Yang; Jing Tang; | iclr | 2026-02-17 |
| 699 | CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present CounselBench, a large-scale benchmark developed with 100 mental health professionals to evaluate and stress-test large language models (LLMs) in realistic help-seeking scenarios. |
YAHAN LI et. al. | iclr | 2026-02-17 |
| 700 | Uncertainty As Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we focus on UQ for the contextual QA task and propose a theoretically grounded approach to quantify \emph{epistemic uncertainty}. |
YAVUZ FARUK BAKMAN et. al. | iclr | 2026-02-17 |
| 701 | IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address this gap, we introduce IndicVisionBench, the first large-scale benchmark centered on the Indian subcontinent.In addition, we release a paired parallel corpus of annotations across 10 Indic languages, creating a unique resource for analyzing cultural and linguistic biases in VLMs. |
ALI FARAZ et. al. | iclr | 2026-02-17 |
| 702 | AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by how humans link information associatively, we propose AssoMem, a novel framework constructing an associative memory graph that anchors dialogue utterances to automatically extracted clues. |
KAI ZHANG et. al. | iclr | 2026-02-17 |
| 703 | Addressing Pitfalls in The Evaluation of Uncertainty Estimation Methods for Natural Language Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose using several alternative risk indicators for risk correlation experiments that improve robustness of empirical assessment of UE algorithms for NLG. |
Mykyta Ielanskyi; Kajetan Schweighofer; Lukas Aichberger; Sepp Hochreiter; | iclr | 2026-02-17 |
| 704 | SAFER: Risk-Constrained Sample-then-Filter in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, prior works unrealistically assume that admissible answers for all instances can be obtained via finite sampling, even for open-ended QA scenarios that lack a fixed and finite solution space. To address this, we introduce a two-stage risk control framework comprising abstention-aware **SA**mpling and conformalized **F**ilt**ER**ing (SAFER). |
Qingni Wang; Yue Fan; Xin Eric Wang; | iclr | 2026-02-17 |
| 705 | Query-Guided Spatial–Temporal–Frequency Interaction for Music Audio–Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, in those methods, the audio input is primarily treated as complementary to video analysis, and the textual question information contributes minimally to audio–visual understanding, as it is typically integrated only in the final stages of reasoning. To address these limitations, we propose a novel Query-guided Spatial–Temporal–Frequency (QSTar) interaction method, which effectively incorporates question-guided clues and exploits the distinctive frequency-domain characteristics of audio signals, alongside spatial and temporal perception, to enhance audio–visual understanding. |
Kun Li; Michael Ying Yang; Sami Sebastian Brandt; | iclr | 2026-02-17 |
| 706 | When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We instead frame abstention as a teachable skill and introduce a pipeline that couples Chain-of-Thought (CoT) supervision with Reinforcement Learning (RL) guided by abstention-aware rewards. |
Xinyu Zhou; Chang Jin; Carsten Eickhoff; Zhijiang Guo; Seyed Ali Bahrainian; | iclr | 2026-02-17 |
| 707 | Same Content, Different Representations: A Controlled Study for Table QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To support detailed analysis, we introduce a diagnostic benchmark with splits along table size, join requirements, query complexity, and schema quality. |
Yue Zhang; Seiji Maekawa; Nikita Bhutani; | iclr | 2026-02-17 |
| 708 | Automating Construction Contract Question Answering Using Large Language Model and Fine-tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
MINGYU ZHANG et. al. | Expert Syst. Appl. | |
| 709 | KenLumachiQuAD – A Question Answering Dataset for Kenyan Luhya Lumarachi Language for Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Question-answering (QA) datasets play a crucial role in testing and training machine learning models, from which we can develop practical end-user applications, such as internet search, dialogue systems, and chatbots. |
Barack Wamkaya Wanjawa; Lawrence Muchemi; Evans Miriti; | East African Journal of Information Technology | 2026-02-16 |
| 710 | IKIA: Image-Knowledge Internalization Assistance Model for Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yurun Bi; Xingang Wang; Yuteng Xiao; Yudong Zhang; | International Journal of Machine Learning and Cybernetics | 2026-02-16 |
| 711 | A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a multi-agent medical QA framework that combines complementary LLMs with evidence retrieval, uncertainty estimation, and bias checks to improve answer reliability. |
Naeimeh Nourmohammadi; Md Meem Hossain; The Anh Han; Safina Showkat Ara; Zia Ush Shamszaman; | arxiv-cs.CL | 2026-02-15 |
| 712 | Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes the Deferred Visual Ingestion (DVI) framework, adopting a demand-side ingestion strategy: the indexing phase performs only lightweight metadata extraction, deferring visual understanding to the moment users pose specific questions. |
Tao Xu; | arxiv-cs.CL | 2026-02-15 |
| 713 | Differentially Private Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Particularly for RAG systems, DP can reduce the usefulness of the augmented contexts leading to increase risk of hallucination from the LLMs. Motivated by these challenges, we present DP-KSA, a novel privacy-preserving RAG algorithm that integrates DP using the propose-test-release paradigm. |
Tingting Tang; James Flemings; Yongqin Wang; Murali Annavaram; | arxiv-cs.CR | 2026-02-15 |
| 714 | SRA: Semantic Relation-Aware Flowchart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the issue, we propose a novel Semantic Relation-Aware (SRA) FlowchartQA approach. |
Xinyu Li; Bowei Zou; Yuchong Chen; Yifan Fan; Yu Hong; | arxiv-cs.MM | 2026-02-14 |
| 715 | ReFilter: Improving Robustness of Retrieval-Augmented Generation Via Gated Filter Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite their effectiveness at modest retrieval scales, these methods often fail to scale gracefully as the number of retrieved candidates k increases: Larger k improves evidence coverage, yet realistic top-k retrieval inevitably contains irrelevant or redundant content and increases the inference cost. To address these limitations, we propose ReFilter, a novel latent-based fusion framework that performs token-level filtering and fusion. |
YIXIN CHEN et. al. | arxiv-cs.CL | 2026-02-13 |
| 716 | Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the Unified Memory Agent (UMA), an end-to-end reinforcement learning framework that unifies memory operations and question answering within a single policy. |
Kehao Zhang; Shangtong Gui; Sheng Yang; Wei Chen; Yang Feng; | arxiv-cs.LG | 2026-02-13 |
| 717 | RoadscapesQA: A Multitask, Multimodal Dataset for Visual Question Answering on Indian Roads Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we describe the data collection and annotation process, present key dataset statistics, and provide initial baselines for image QA tasks using vision-language models. |
Vijayasri Iyer; Maahin Rathinagiriswaran; Jyothikamalesh S; | arxiv-cs.CV | 2026-02-13 |
| 718 | TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing table QA systems rarely provide fine-grained attribution, so even correct answers often lack verifiable grounding, limiting trust in high-stakes settings. We address this with TraceBack, a modular multi-agent framework for scalable, cell-level attribution in single-table QA. |
TEJAS ANVEKAR et. al. | arxiv-cs.CL | 2026-02-13 |
| 719 | Who Is The Richest Club in The Championship? Detecting and Rewriting Underspecified Questions Improve QA Performance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We argue that this gap is partly due to underspecified questions – queries whose interpretation cannot be uniquely determined without additional context. To test this hypothesis, we introduce an LLM-based classifier to identify underspecified questions and apply it to several widely used QA datasets, finding that 16% to over 50% of benchmark questions are underspecified and that LLMs perform significantly worse on them. |
Yunchong Huang; Gianni Barlacchi; Sandro Pezzelle; | arxiv-cs.CL | 2026-02-12 |
| 720 | MultiCube-RAG for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Built on the cube structure, we propose MultiCube-RAG, a training-free method consisting of multiple cubes for multi-step reasoning and retrieval. |
JIMENG SHI et. al. | arxiv-cs.CL | 2026-02-11 |
| 721 | RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Multimodal large language models (MLLMs) are increasingly adopted in remote sensing (RS) and have shown strong performance on tasks such as RS visual grounding (RSVG), RS visual question answering (RSVQA), and multimodal dialogue. |
ZIHUI ZHOU et. al. | arxiv-cs.CV | 2026-02-11 |
| 722 | PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce PulseLM, a large-scale PPG-text dataset designed to bridge raw PPG waveforms and natural language through a unified, closed-ended question answering (QA) formulation. |
HUNG MANH PHAM et. al. | arxiv-cs.CL | 2026-02-10 |
| 723 | Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets spanning multiple domains. |
Klejda Alushi; Jan Strich; Chris Biemann; Martin Semmann; | arxiv-cs.CL | 2026-02-10 |
| 724 | AnalyticsGPT: An LLM Workflow for Scientometric Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces AnalyticsGPT, an intuitive and efficient large language model (LLM)-powered workflow for scientometric question answering. |
Khang Ly; Georgios Cheirmpos; Adrian Raudaschl; Christopher James; Seyed Amin Tabatabaei; | arxiv-cs.CL | 2026-02-10 |
| 725 | CAPID: Context-Aware PII Detection for Question-Answering Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To achieve privacy-preserving PII detection, we propose CAPID, a practical approach that fine-tunes a locally owned small language model (SLM) that filters sensitive information before it is passed to LLMs for QA. |
MARIIA PONOMARENKO et. al. | arxiv-cs.CR | 2026-02-10 |
| 726 | Engineering Trustworthy Retrieval-Augmented Generation for EU Electricity Market Regulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents the design of a domain-specific Retrieval-Augmented Generation (RAG) system for EU electricity market regulations, explicitly engineered to deliver source-grounded, traceable and low-hallucination answers. |
Șener Ali; Simona-Vasilica Oprea; Adela Bâra; | Electronics | 2026-02-10 |
| 727 | Vista: Scene-Aware Optimization for Streaming Video Question Answering Under Post-Hoc Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Vista, a novel framework for scene-aware streaming video QA that enables efficient and scalable reasoning over continuous video streams. |
HAOCHENG LU et. al. | arxiv-cs.CV | 2026-02-09 |
| 728 | CoRect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through layer-wise analysis, we attribute this failure to a parametric suppression phenomenon: specifically, in deep layers, certain FFN layers overwrite context-sensitive representations with memorized priors. To address this, we propose CoRect (Context-Aware Logit Contrast for Hidden State Rectification). |
Xuhua Ma; Richong Zhang; Zhijie Nie; | arxiv-cs.CL | 2026-02-08 |
| 729 | Long-Context Long-Form Question Answering for Legal Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we address the challenges of long-context question answering in context of long-form answers given the idiosyncrasies of legal documents. |
ANAGHA KULKARNI et. al. | arxiv-cs.CL | 2026-02-06 |
| 730 | ViHERMES: A Graph-Grounded Multihop Question Answering Benchmark and System for Vietnamese Healthcare Regulations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce the Vietnamese Healthcare Regulations-Multihop Reasoning Dataset (ViHERMES), a benchmark designed for multihop QA over Vietnamese healthcare regulatory documents. |
LONG S. T. NGUYEN et. al. | arxiv-cs.CL | 2026-02-06 |
| 731 | MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Abstract This work introduces MediQAl, a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and reasoning over real-world clinical scenarios. |
Adrien Bazoge; | Scientific Data | 2026-02-05 |
| 732 | Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say I Don’t Know Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We find that although accuracy gains from decomposition diminish in frontier models, disagreements between prompting regimes remain highly indicative of potential errors. |
Dhruv Madhwal; Lyuxin David Zhang; Dan Roth; Tomer Wolfson; Vivek Gupta; | arxiv-cs.CL | 2026-02-04 |
| 733 | Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a formal problem formulation for \textit{Reliable} Audio-Visual Question Answering ($\mathcal{R}$-AVQA), where we prefer abstention over answering incorrectly. |
DINH PHU TRAN et. al. | arxiv-cs.LG | 2026-02-04 |
| 734 | RA-QA: Towards Respiratory Audio-based Health Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This new data resource contains about 7.5 million QA pairs spanning more than 60 attributes and three question types: single verification, multiple choice, and open-ended questions. Building upon this dataset, we introduce a novel benchmark that compares audio-text generation models with traditional audio classifiers to evaluate their respective performance. |
Gaia A. Bertolino; Yuwei Zhang; Tong Xia; Domenico Talia; Cecilia Mascolo; | arxiv-cs.SD | 2026-02-04 |
| 735 | No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a new framework that systematically transforms single-hop cultural questions into multi-hop reasoning chains spanning six clue types (e.g., commonsense, temporal, geographical). |
Vynska Amalia Permadi; Xingwei Tan; Nafise Sadat Moosavi; Nikos Aletras; | arxiv-cs.CL | 2026-02-03 |
| 736 | OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Long-horizon omnimodal question answering answers questions by reasoning over text, images, audio, and video. Despite recent progress on OmniLLMs, low-resource long audio-video QA still suffers from costly dense encoding, weak fine-grained retrieval, limited proactive planning, and no clear end-to-end optimization.To address these issues, we propose OmniRAG-Agent, an agentic omnimodal QA method for budgeted long audio-video reasoning. |
YIFAN ZHU et. al. | arxiv-cs.CL | 2026-02-03 |
| 737 | JSynFlow: Japanese Synthesised Flowchart Visual Question Answering Dataset Built with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, developing VLMs with precise flowchart understanding requires large-scale datasets of flowchart images and corresponding text, the creation of which is highly time-consuming. To address this challenge, we introduce JSynFlow, a synthesised visual QA dataset for Japanese flowcharts, generated using large language models (LLMs). |
Hiroshi Sasaki; | arxiv-cs.CV | 2026-02-03 |
| 738 | ST-Raptor: An Agentic System for Semi-Structured Table QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing Text-to-SQL methods typically require converting semi-structured tables into structured formats, inevitably leading to information loss, while approaches like Text-to-Code and multimodal LLM-based QA struggle with complex layouts and often yield inaccurate answers. To address these limitations, we present ST-Raptor, an agentic system for semi-structured table QA. |
JINXIU QU et. al. | arxiv-cs.AI | 2026-02-03 |
| 739 | Improving Episodic Few-shot Visual Question Answering Via Spatial and Frequency Domain Dual-calibration Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Jing Zhang; Yifan Wei; Yunzuo Hu; Zhe Wang; | Pattern Recognit. | 2026-02-01 |
| 740 | P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our contributions include: (1) LoRA-based fine-tuning of LLaMA-3.2-1B-Instruct for biomedical QA, (2) introduction of P-RAG with Chain-of-Thought prompting, and (3) state-of-the-art results on PubMedQA and 2WikiMultihopQA. |
Xingda Lyu; Gongfu Lyu; Zitai Yan; Yuxin Jiang; | arxiv-cs.CL | 2026-02-01 |
| 741 | CRAFT: Calibrated Reasoning with Answer-Faithful Traces Via Reinforcement Learning for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional chain-of-thought generation often deviates from required structured output formats, leading to incomplete or malformed structured content. To address these challenges, we propose CRAFT (Calibrated Reasoning with Answer-Faithful Traces), a Group Relative Policy Optimization (GRPO) based reinforcement learning framework that trains models to perform faithful reasoning during response generation. |
YU LIU et. al. | arxiv-cs.CL | 2026-02-01 |
| 742 | TDR2A: Time-sensitive Decomposition-retrieval-reorganization Agent for Temporal Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Zi-Cheng Huang; Xin-Hui Tu; Tingting He; Wei-Zhong Zhao; | Expert Syst. Appl. | 2026-02-01 |
| 743 | PARSE: An Open-Domain Reasoning Question Answering Benchmark for Persian Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce PARSE, the first open-domain Persian reasoning QA benchmark, containing 10,800 questions across Boolean, multiple-choice, and factoid formats, with diverse reasoning types, difficulty levels, and answer structures. |
Jamshid Mozafari; Seyed Parsa Mousavinasab; Adam Jatowt; | arxiv-cs.CL | 2026-02-01 |
| 744 | Understanding QA Generation: Extracting Parametric and Contextual Knowledge with CQA for Low Resource Bangla Language Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our work not only introduces a novel framework for analyzing knowledge sources in Bangla QA but also uncovers critical findings that open up broader directions for counterfactual reasoning in low-resource language settings. |
Umme Abira Azmary; MD Ikramul Kayes; Swakkhar Shatabda; Farig Yousuf Sadeque; | arxiv-cs.CL | 2026-02-01 |
| 745 | MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, current approaches rely on query-agnostic document representations that overlook salient content and use static top-k evidence selection, which fails to adapt to the uncertain distribution of relevant information. To address these limitations, we propose the Multimodal Adaptive Retrieval-Augmented (MARA) framework, which introduces query-adaptive mechanisms to both retrieval and generation. |
HUI WU et. al. | arxiv-cs.IR | 2026-02-01 |
| 746 | Explainable Visual Question Answering: A Survey on Methods, Datasets and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
YAXIAN WANG et. al. | Inf. Fusion | 2026-02-01 |
| 747 | ETV-Attack: Efficient Text-driven Visual-variable Adversarial Attacks on Visual Question Answering with Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
QUANXING XU et. al. | Pattern Recognit. | 2026-02-01 |
| 748 | Multigranularity-3DQA: Dynamic Multi-granularity Perception and Gated Dual-stage Encoder-decoder for 3D Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Letao Zhang; Weibing Wan; Ting-Syun Huang; Zhijun Fang; Hongbing Zhang; | Expert Syst. Appl. | 2026-02-01 |
| 749 | A Framework of Knowledge Graph-Enhanced Large Language Model Based on Global Planning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Knowledge graphs (KGs) can provide structured knowledge to assist large language models (LLMs) in interpretable reasoning. Knowledge graph question answering (KGQA) is a typical … |
YADING LI et. al. | IEEE Transactions on Knowledge and Data Engineering | 2026-02-01 |
| 750 | A Visual-textual Mutual Guidance Fusion Network for Remote Sensing Visual Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
HAOLIN LIU et. al. | Pattern Recognit. | 2026-02-01 |
| 751 | MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose MedSpeak, a novel knowledge graph-aided ASR error correction framework that refines noisy transcripts and improves downstream answer prediction by leveraging both semantic relationships and phonetic information encoded in a medical knowledge graph, together with the reasoning power of LLMs. |
YUTONG SONG et. al. | arxiv-cs.CL | 2026-01-31 |
| 752 | DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DeALOG, a decentralized multi-agent framework for multimodal question answering. |
Abhijit Chakraborty; Ashish Raj Shekhar; Shiven Agarwal; Vivek Gupta; | arxiv-cs.CL | 2026-01-31 |
| 753 | TSAQA: Time Series Analysis Question And Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce TSAQA, a novel unified benchmark designed to broaden task coverage and evaluate diverse temporal analysis capabilities. |
BAOYU JING et. al. | arxiv-cs.AI | 2026-01-30 |
| 754 | Gender Disparities in StackOverflow’s Community-Based Question Answering: A Matter of Quantity Versus Quality Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we investigate whether answer quality is influenced by gender using a combination of human evaluations and automated assessments powered by Large Language Models. |
Maddalena Amendola; Cosimo Rulli; Carlos Castillo; Andrea Passarella; Raffaele Perego; | arxiv-cs.CY | 2026-01-30 |
| 755 | Benchmarking Uncertainty Calibration in Large Language Model Long-Form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce the first large-scale benchmark for evaluating UQ metrics in reasoning-demanding QA studying calibration of UQ methods, providing an extensible open-source framework to reproducibly assess calibration. |
Philip Müller; Nicholas Popovič; Michael Färber; Peter Steinbach; | arxiv-cs.CL | 2026-01-30 |
| 756 | CE-GOCD: Central Entity-Guided Graph Optimization for Community Detection to Augment LLM Scientific Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This impairs the LLM’s comprehension of scientific literature, hindering the comprehensiveness and specificity of its responses. To address this, we propose Central Entity-Guided Graph Optimization for Community Detection (CE-GOCD), a method that augments LLMs’ scientific question answering by explicitly modeling and leveraging semantic substructures within academic knowledge graphs. |
JIAYIN LAN et. al. | arxiv-cs.CL | 2026-01-29 |
| 757 | Ontology-grounded Knowledge Graphs for Mitigating Hallucinations in Large Language Models for Clinical Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Mohamed Ali; Zaki Taha; Mohamed Mabrouk Morsey; | Journal of Biomedical Informatics | 2026-01-28 |
| 758 | SRCR: Faithful Structured Reasoning with Curriculum Reinforcement Learning for Explainable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
YUE FAN et. al. | Information Processing & Management | 2026-01-27 |
| 759 | Query-Guided Spatial-Temporal-Frequency Interaction for Music Audio-Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, in those methods, the audio input is primarily treated as complementary to video analysis, and the textual question information contributes minimally to audio–visual understanding, as it is typically integrated only in the final stages of reasoning. To address these limitations, we propose a novel Query-guided Spatial–Temporal–Frequency (QSTar) interaction method, which effectively incorporates question-guided clues and exploits the distinctive frequency-domain characteristics of audio signals, alongside spatial and temporal perception, to enhance audio–visual understanding. |
Kun Li; Michael Ying Yang; Sami Sebastian Brandt; | arxiv-cs.CV | 2026-01-27 |
| 760 | V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent introspective detection methods, particularly uncertainty-based approaches, offer computational efficiency but are fundamentally indirect, as they estimate predictive uncertainty for an image-question pair rather than verifying the factual correctness of a specific answer. To address this limitation, we propose Visual Logical Loop Verification (V-Loop), a training-free and plug-and-play framework for hallucination detection in medical VQA. |
Mengyuan Jin; Zehui Liao; Yong Xia; | arxiv-cs.CV | 2026-01-26 |
| 761 | PaperSearchQA: Learning to Search and Reason Over Scientific Papers with RLVR Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers — this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. |
JAMES BURGESS et. al. | arxiv-cs.LG | 2026-01-26 |
| 762 | UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Models (LLMs) offer a powerful reasoning engine for this task but struggle to natively process these heterogeneous, non-textual data types. To address this, we propose UniPACT (Unified Prognostic Question Answering for Clinical Time-series), a unified framework for prognostic question answering that bridges this modality gap. |
Jialu Tang; Tong Xia; Yuan Lu; Aaqib Saeed; | arxiv-cs.LG | 2026-01-25 |
| 763 | Beyond Factual QA: Mentorship-Oriented Question Answering Over Long-Form Multilingual Content Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce MentorQA, the first multilingual dataset and evaluation framework for mentorship-focused question answering from long-form videos, comprising nearly 9,000 QA pairs from 180 hours of content across four languages. |
Parth Bhalerao; Diola Dsouza; Ruiwen Guan; Oana Ignat; | arxiv-cs.CL | 2026-01-23 |
| 764 | DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, RAG is often challenged by reasoning-intensive question-answering (QA), since common retrieval methods like cosine similarity maximize relevance at the cost of introducing redundant content, which can reduce information recall. To address this, we introduce Diversity-Focused Retrieval-Augmented Generation (DF-RAG), which systematically incorporates diversity into the retrieval step to improve performance on complex, reasoning-intensive QA benchmarks. |
SAADAT HASAN KHAN et. al. | arxiv-cs.CL | 2026-01-23 |
| 765 | ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ManuRAG, an innovative multi-modal RAG framework designed for manufacturing QA, incorporating specialized techniques to improve answer accuracy, reliability, and interpretability. |
Yunqing Li; Zihan Dong; Farhad Ameri; Jianbang Zhang; | arxiv-cs.CE | 2026-01-21 |
| 766 | LogicScore: Fine-grained Logic Evaluation of Conciseness, Completeness, and Determinateness in Attributed Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Current evaluation methods for Attributed Question Answering (AQA) suffer from \textit{attribution myopia}: they emphasize verification of isolated statements and their … |
ZHICHAO YAN et. al. | ArXiv | 2026-01-21 |
| 767 | \textsc{LogicScore}: Fine-grained Logic Evaluation of Conciseness, Completeness, and Determinateness in Attributed Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Consequently, Large Language Models (LLMs) often produce factually grounded yet logically incoherent responses with elusive deductive gaps. To mitigate this limitation, we present \textsc{LogicScore}, a unified evaluation framework that shifts the paradigm from local assessment to global reasoning scrutiny. |
ZHICHAO YAN et. al. | arxiv-cs.CL | 2026-01-21 |
| 768 | ManuRAG: Multi-modal Retrieval Augmented Generation for Manufacturing Question Answering (Early Version) Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data, such as text, … |
Yunqing Li; Zihan Dong; Farhad Ameri; Jianbang Zhang; | ArXiv | 2026-01-21 |
| 769 | Self-Correction Distillation for Structured Data Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. |
YUSHAN ZHU et. al. | aaai | 2026-01-20 |
| 770 | CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing benchmarks for evaluating the reliability of Multimodal Large Language Models (MLLMs) predominantly focus on textual or visual modalities with a primary emphasis on English, which creates a gap in evaluation when processing multilingual input, especially in speech. To bridge this gap, we propose a novel Cross-lingual and Cross-modal Factuality benchmark (CCFQA). |
YEXING DU et. al. | aaai | 2026-01-20 |
| 771 | NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent works attempt to overcome these limitations through heuristic approaches; however, they lack explicit mechanisms for encoding temporal relationships and fail to provide any formal guarantees that the sampled context actually encodes the compositional or causal logic required by the question. To address these foundational gaps, we introduce NeuS-QA, a training-free, plug-and-play neuro-symbolic pipeline for LVQA. |
SAHIL SHAH et. al. | aaai | 2026-01-20 |
| 772 | QKVQA: Question-Focused Filtering for Knowledge-based VQA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Visual Question Answering (VQA) is the task of answering questions based on image content. Building upon this, Knowledge-Based VQA (KB-VQA) requires models to answer questions … |
WEI YE et. al. | ArXiv | 2026-01-20 |
| 773 | EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In contrast, real-world deployment inevitably encounters domain shifts, where target domains differ substantially in both visual style and semantic content. To bridge this gap, we introduce EgoCross, a comprehensive benchmark designed to evaluate the cross-domain generalization of MLLMs in EgocentricQA. |
YANJUN LI et. al. | aaai | 2026-01-20 |
| 774 | PKR-QA: A Benchmark for Procedural Knowledge Reasoning with Knowledge Module Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To generate question-answer pairs, we design graph traversal templates where each template is applied systematically over PKG. To enable interpretable reasoning, we propose a neurosymbolic approach called Knowledge Module Learning (KML), which learns procedural relations via neural modules and composes them for structured reasoning with LLMs. |
THANH-SON NGUYEN et. al. | aaai | 2026-01-20 |
| 775 | Collaborative Enhancement of Large and Small Models for Question Answering Via Dual Knowledge Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our statistical analysis reveals a complementary phenomenon between large language model-based question answering (QA) and small model-based QA. To facilitate dual knowledge transfer between these two paradigms, this paper introduces a collaborative enhancement method of large and small models for question answering. |
Shaofei Wang; Yunan Liu; Xiaolan Tang; Wenlong Chen; | aaai | 2026-01-20 |
| 776 | Improving Long-Context Summarization with Multi-Granularity Retrieval Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Notably, the human brain naturally integrates and summarizes prior knowledge upon reading a given text, progressively formulating a comprehensive understanding. Motivated by this cognitive process, we propose the Hierarchical Two-Stage Summarization-based Information Retrieval (HTSIR) method, which preprocesses the corpus prior to retrieval, summarizes continuous texts to obtain integrated information, and constructs a retrieval tree with varying summary granularities. |
Xueyu Chen; Kaitao Song; Zifan Song; Dongsheng Li; Cairong Zhao; | aaai | 2026-01-20 |
| 777 | Domain-Adaptation Through Synthetic Data: Fine-Tuning Large Language Models for German Law Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents an effective method for adapting advanced LLMs to German legal question answering through a novel synthetic data generation approach. |
ALI HAMZA BASHIR et. al. | arxiv-cs.CL | 2026-01-20 |
| 778 | DomainCQA: Crafting Knowledge-Intensive QA from Domain-Specific Charts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DomainCQA, a framework for constructing domain-specific CQA benchmarks that emphasize both visual comprehension and knowledge-intensive reasoning. |
YUJING LU et. al. | aaai | 2026-01-20 |
| 779 | GeoX-Bench: Benchmarking Cross-View Geo-Localization and Pose Estimation Capabilities of Large Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large multimodal models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks, however their knowledge and abilities in the cross-view geo-localization and pose estimation domains remain unexplored, despite potential benefits for navigation, autonomous driving, outdoor robotics, etc. To bridge this gap, we introduce GeoX-Bench, a comprehensive Benchmark designed to explore and evaluate the capabilities of LMMs in cross-view Geo-localization and pose estimation. |
YUSHUO ZHENG et. al. | aaai | 2026-01-20 |
| 780 | Mnemosyne: Accelerating Multi-Hop Question Answering Via Cache Hit Order Fitting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods focus on the internal structure and ignore the misalignment between the queries’ arrival order and cache hit order. To tackle this, we propose Mnemosyne, a cache hit order fitting method designed to accelerate the RAG progress for MHQA. |
Haizhou Du; Jiujiu Li; Dongyang Li; Luobin Huang; Lisheng Wang; | aaai | 2026-01-20 |
| 781 | MIRAGE: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a result, these approaches can lead to error accumulation throughout the reasoning chain, which significantly limits its effectiveness in medical question-answering (QA) tasks where both accuracy and traceability are critical requirements. To address these challenges, we propose MIRAGE (Multi-path Inference with Retrieval-Augmented Graph Exploration), a novel test-time scalable reasoning framework that performs dynamic multi-path inference over structured medical knowledge graphs. |
KAIWEN WEI et. al. | aaai | 2026-01-20 |
| 782 | Teaching Large Language Models to Maintain Contextual Faithfulness Via Synthetic Tasks and Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framework, CANOE, to reduce faithfulness hallucinations of LLMs across different downstream tasks without human annotations. |
SHUZHENG SI et. al. | aaai | 2026-01-20 |
| 783 | Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by semantic parsing methods, we propose PDRR: a four-stage framework consisting of Predict, Decompose, Retrieve, and Reason. |
Yihua Zhu; Qianying Liu; Akiko Aizawa; Hidetoshi Shimodaira; | aaai | 2026-01-20 |
| 784 | EvalQAG: A Framework for Automatic Complex QA Generation and A Benchmark QA Dataset for Policy Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce EvalQAG, a framework for generating high-quality QA pairs from renewable energy policy documents. |
Kirtan Brijeshbhai Soni; Krish Rupapara; Arpit Rana; Ghanshyam Verma; Paul Buitelaar; | aaai | 2026-01-20 |
| 785 | DigimonGPT: An Evolvable Agent with Hierarchical Human-like Memory for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the challenge, we introduce DigimonGPT, an evolvable VideoQA agent inspired by cognitive psychology. |
BORUI LI et. al. | aaai | 2026-01-20 |
| 786 | Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel RAG approach called HGRAG for MHQA that achieves cross-granularity integration of structural and semantic information via hypergraphs. |
Changjian Wang; Weihong Deng; Weili Guan; Quan Lu; Ning Jiang; | aaai | 2026-01-20 |
| 787 | STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Vision-Language Models (VLMs) have been applied to autonomous driving to support decision-making in complex real-world scenarios. |
Keishi Ishihara; Kento Sasaki; Tsubasa Takahashi; Daiki Shiono; Yu Yamaguchi; | aaai | 2026-01-20 |
| 788 | StreamKV: Streaming Video Question-Answering with Segment-based KV Cache Retrieval and Compression IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose StreamKV, a training-free framework that seamlessly equips Video-LLMs with advanced KV cache retrieval and compression. |
YILONG CHEN et. al. | aaai | 2026-01-20 |
| 789 | OIDA-QA: A Multimodal Benchmark for Analyzing The Opioid Industry Documents Archive Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The complexity, multimodal nature, and specialized characteristics of these healthcare-related legal and corporate documents necessitate more advanced methods and models tailored to specific data types and detailed annotations, ensuring the precision and professionalism in the analysis. In this paper, we tackle this challenge by organizing the original dataset according to document attributes and constructing a benchmark with 400k training documents and 10k for testing. |
XUAN SHEN et. al. | aaai | 2026-01-20 |
| 790 | MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current methods often struggle with balancing knowledge retention, adaptation, and robust feature representation. To address these challenges, we propose a novel framework with adaptive memory allocation and global noise filtering called MacVQA for visual question answering. |
ZHIFEI LI et. al. | aaai | 2026-01-20 |
| 791 | Knowledge-based Question Answering Using Graph Neural Networks and Contextual Language Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Abstract This work introduces a novel question answering (QA) framework that integrates commonsense knowledge from ConceptNet with deep contextual embeddings from BERT using a graph neural network for structured reasoning. |
Mohamed Samir; Naglaa Fathy; Walaa Gad; | Scientific Reports | 2026-01-20 |
| 792 | ChartAttack: Testing The Vulnerability of LLMs to Malicious Prompting in Chart Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce ChartAttack, a novel framework for evaluating how MLLMs can be misused to generate misleading charts at scale. |
Jesus-German Ortiz-Barajas; Jonathan Tonglet; Vivek Gupta; Iryna Gurevych; | arxiv-cs.CL | 2026-01-19 |
| 793 | Pardon? Evaluating Conversational Repair in Large Audio-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce a repair-aware evaluation setting that explicitly distinguishes between answerable and unanswerable audio inputs. |
SHUANGHONG HUANG et. al. | arxiv-cs.CL | 2026-01-19 |
| 794 | BioPulse-QA: A Dynamic Biomedical Question-Answering Benchmark for Evaluating Factuality, Robustness, and Bias in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: They also carry increasing risk of data leakage due to overlap with model pretraining corpora and often overlook critical dimensions such as robustness to linguistic variation and potential demographic biases. Materials and Methods: To address these gaps, we introduce BioPulse-QA, a benchmark that evaluates LLMs on answering questions from newly published biomedical documents including drug labels, trial protocols, and clinical guidelines. |
KRITI BHATTARAI et. al. | arxiv-cs.CL | 2026-01-18 |
| 795 | Augmenting Question Answering with A Hybrid RAG Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce Structured-Semantic RAG (SSRAG), a hybrid architecture that enhances QA quality by integrating query augmentation, agentic routing, and a structured retrieval mechanism combining vector and graph based techniques with context unification. |
TIANYI YANG et. al. | arxiv-cs.CL | 2026-01-18 |
| 796 | SolarGPT-QA: A Domain-Adaptive Large Language Model for Educational Question Answering in Space Weather and Heliophysics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce SolarGPT-QA, a question answering system based on a domain-adapted large language model built on the LLaMA-3 base model. |
Santosh Chapagain; MohammadReza EskandariNasab; Onur Vural; Shah Muhammad Hamdi; Soukaina Filali Boubrahimi; | arxiv-cs.LG | 2026-01-17 |
| 797 | AVIR: Adaptive Visual In-Document Retrieval for Efficient Multi-Page Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Multi-page Document Visual Question Answering (MP-DocVQA) remains challenging because long documents not only strain computational resources but also reduce the effectiveness of the attention mechanism in large vision-language models (LVLMs). We tackle these issues with an Adaptive Visual In-document Retrieval (AVIR) framework. |
Zongmin Li; Yachuan Li; Lei Kang; Dimosthenis Karatzas; Wenkang Ma; | arxiv-cs.CV | 2026-01-17 |
| 798 | Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For multi-hop QA tasks, current iterative approaches predominantly rely on LLMs to self-guide and plan multi-step exploration paths during retrieval, leading to substantial challenges in maintaining reasoning coherence across steps from inaccurate query decomposition and error propagation. To address these issues, we introduce Reasoning Tree Guided RAG (RT-RAG), a novel hierarchical framework for complex multi-hop QA. |
YULING SHI et. al. | arxiv-cs.CL | 2026-01-16 |
| 799 | A Topic-aware Evaluation of ChatGPT’s Semantic Alignment with Community Answers Using BERTScore and BERTopic Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language models are increasingly deployed in everyday question-answering tasks, yet their performance across diverse, real-world queries remains underexplored. This study … |
Mashael M. Alsulami; | PeerJ Comput. Sci. | 2026-01-16 |
| 800 | From Single to Multi-Agent Reasoning: Advancing GeneGPT for Genomics QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We replicate GeneGPT and propose GenomAgent, a multi-agent framework that efficiently coordinates specialized agents for complex genomics queries. |
Kimia Abedini; Farzad Shami; Gianmaria Silvello; | arxiv-cs.AI | 2026-01-15 |
| 801 | ReGraM: Region-First Knowledge Graph Reasoning for Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We argue that the core challenge lies not in expanding access to knowledge, but in identifying and reasoning over the appropriate subset of evidence for each query. |
Chaerin Lee; Sohee Park; Hyunsik Na; Daseon Choi; | arxiv-cs.CL | 2026-01-14 |
| 802 | An Electronic Product Carbon Footprint Dataset for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work establishes a foundation for training advanced language models to automate aggregation and standardization of emissions data for ICT systems. |
Kaiwen Zhao; Ajesh Koyatan Chathoth; Bharathan Balaji; Stephen Lee; | Scientific Data | 2026-01-14 |
| 803 | EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering Over Heterogeneous Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. |
LINGFEI QIAN et. al. | arxiv-cs.CL | 2026-01-14 |
| 804 | Beyond Structured Knowledge: Performance Boundaries of ChatGPT in Geological-hazard Question Answering and The Need for Human-in-the-loop Oversight Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We conduct a version-specific evaluation of ChatGPT-4o for geological-hazard question answering using a transparent, rubric-based design. |
SAIER WU et. al. | Frontiers in Earth Science | 2026-01-13 |
| 805 | Enhancing Large Language Models for Knowledge Graph Question Answering Via Multi-granularity Knowledge Injection and Structured Reasoning Path-augmented Prompting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Chuanyang Gong; Zhihua Wei; Wenhao Tao; Duoqian Miao; | Information Processing & Management | 2026-01-13 |
| 806 | STAGE: A Benchmark for Knowledge Graph Construction, Question Answering, and In-Script Role-Playing Over Movie Screenplays Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce STAGE (Screenplay Text, Agents, Graphs and Evaluation), a unified benchmark for narrative understanding over full-length movie screenplays. |
QIUYU TIAN et. al. | arxiv-cs.CL | 2026-01-13 |
| 807 | Judging Against The Reference: Uncovering Knowledge-Driven Failures in LLM-Judges on QA Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We identify a critical failure mode of such reference-based LLM QA evaluation: when the provided reference conflicts with the judge model’s parametric knowledge, the resulting scores become unreliable, substantially degrading evaluation fidelity. To study this phenomenon systematically, we introduce a controlled swapped-reference QA framework that induces reference-belief conflicts. |
DONGRYEOL LEE et. al. | arxiv-cs.CL | 2026-01-12 |
| 808 | Fine-Tuning Vs. RAG for Multi-Hop Question Answering with Novel Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we systematically compare parametric and non-parametric knowledge injection methods for open-domain multi-hop question answering. |
Zhuoyi Yang; Yurun Song; Iftekhar Ahmed; Ian Harris; | arxiv-cs.CL | 2026-01-11 |
| 809 | Efficient Visual Question Answering Pipeline for Autonomous Driving Via Scene Region Compression Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This focus limits their practical deployment in real-time autonomous driving scenarios. To tackle this issue, we propose an efficient VLM framework for autonomous driving VQA tasks, SRC-Pipeline. |
Yuliang Cai; Dongqiangzi Ye; Zitian Chen; Chongruo Wu; | arxiv-cs.CV | 2026-01-11 |
| 810 | UETQuintet at BioCreative IX – MedHopQA: Enhancing Biomedical QA with Selective Multi-hop Reasoning and Contextual Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a model designed to effectively address both direct and sequential questions. |
Quoc-An Nguyen; Thi-Minh-Thu Vu; Bich-Dat Nguyen; Dinh-Quang-Minh Tran; Hoang-Quynh Le; | arxiv-cs.CL | 2026-01-11 |
| 811 | FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose FinCards, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. |
YIXI ZHOU et. al. | arxiv-cs.IR | 2026-01-11 |
| 812 | N2N-GQA: Noise-to-Narrative for Graph-Based Table-Text Question Answering Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our key insight is that multi-hop reasoning requires understanding relationships between evidence pieces: by modeling documents as graph nodes with semantic relationships as edges, we identify bridge documents connecting reasoning steps, a capability absent in list-based retrieval. |
Mohamed Sharafath; Aravindh Annamalai; Ganesh Murugan; Aravindakumar Venugopalan; | arxiv-cs.CL | 2026-01-10 |
| 813 | Do Language Models Reason Across Languages? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a simple two-hop question answering setting, where answering a question requires making inferences over two multilingual documents. |
Yan Meng; Wafaa Mohammed; Christof Monz; | arxiv-cs.CL | 2026-01-10 |
| 814 | VideoAuto-R1: Video Auto Reasoning Via Thinking Once, Answering Twice IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we first demonstrate that for RL-trained video models, direct answering often matches or even surpasses CoT performance, despite CoT producing step-by-step analyses at a higher computational cost. Motivated by this, we propose VideoAuto-R1, a video understanding framework that adopts a reason-when-necessary strategy. |
SHUMING LIU et. al. | arxiv-cs.CV | 2026-01-08 |
| 815 | A Lightweight and Explainable Vision-Language Framework for Crop Disease Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work presents a lightweight vision-language framework for crop and disease identification from leaf images. |
Md. Zahid Hossain; Most. Sharmin Sultana Samu; Md. Rakibul Islam; Md. Siam Ansary; | arxiv-cs.CV | 2026-01-08 |
| 816 | From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CuCu, an automated multi-agent LLM framework that transforms national textbook curricula into open-ended, culture-specific question-answer pairs. |
Haneul Yoo; Won Ik Cho; Geunhye Kim; Jiyoon Han; | arxiv-cs.CL | 2026-01-08 |
| 817 | DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DisastQA, a large-scale benchmark of 3,000 rigorously verified questions (2,000 multiple-choice and 1,000 open-ended) spanning eight disaster types. |
ZHITONG CHEN et. al. | arxiv-cs.CL | 2026-01-07 |
| 818 | From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Self-Graph Reasoning (SGR), a framework that enables LLMs to explicitly represent their reasoning process as a structured graph before producing the final answer. |
YINGJIAN CHEN et. al. | arxiv-cs.CL | 2026-01-07 |
| 819 | Self-MedRAG: A Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Retrieval-Augmented Generation (RAG) mitigates these issues by incorporating external knowledge, conventional single-shot retrieval often fails to resolve complex biomedical queries requiring multi-step inference. To address this, we propose Self-MedRAG, a self-reflective hybrid framework designed to mimic the iterative hypothesis-verification process of clinical reasoning. |
Jessica Ryan; Alexander I. Gumilang; Robert Wiliam; Derwin Suhartono; | arxiv-cs.IR | 2026-01-07 |
| 820 | BioPIE: A Biomedical Protocol Information Extraction Dataset for High-Reasoning-Complexity Experiment Question Answer Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing biomedical datasets focus on general or coarsegrained knowledge and thus fail to support the fine-grained experimental reasoning demanded by HID and MSR. To address this gap, we introduce Biomedical Protocol Information Extraction Dataset (BioPIE), a dataset that provides procedure-centric KGs of experimental entities, actions, and relations at a scale that supports reasoning over biomedical experiments across protocols. |
HAOFEI HOU et. al. | arxiv-cs.AI | 2026-01-07 |
| 821 | EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering for Enhanced Alignment and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present EpiQAL, the first diagnostic benchmark for epidemiological question answering across diverse diseases, comprising three subsets built from open-access literature. |
MINGYANG WEI et. al. | arxiv-cs.CL | 2026-01-06 |
| 822 | LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We address the scarcity of long-context QA resources for Indic languages by introducing LittiChoQA, the largest literary QA dataset to date covering many languages spoken in the Gangetic plains of India. |
Aarya Khandelwal; Ritwik Mishra; Rajiv Ratn Shah; | arxiv-cs.CL | 2026-01-06 |
| 823 | When Do Tools and Planning Help Large Language Models Think? A Cost- and Latency-Aware Benchmark Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Modern large language models (LLMs) increasingly rely on inference-time planning and external tools to improve reasoning. We benchmark this behavior on two real-world settings: … |
S. Ghoshal; Ali Al-Bustami; | SoutheastCon 2026 | 2026-01-06 |
| 824 | SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. |
JUNLI LIANG et. al. | arxiv-cs.CL | 2026-01-06 |
| 825 | Question Answering for Multi-Release Systems: A Case Study at Ciena Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by the observed inaccuracy of state-of-the-art question-answering techniques on multi-release system documents, we propose QAMR, a chatbot designed to answer questions across multi-release system documentation. |
PARHAM KHAMSEPOUR et. al. | arxiv-cs.SE | 2026-01-05 |
| 826 | Crop GraphRAG: Pest and Disease Knowledge Base Q&A System for Sustainable Crop Protection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By mitigating the limitations of large language models in specialized agricultural contexts, this study provides a pragmatic tool for intelligent QA in the agricultural domain and advances the application of AI in crop protection. |
HAO WU et. al. | Frontiers in Plant Science | 2026-01-05 |
| 827 | When Do Tools and Planning Help LLMs Think? A Cost- and Latency-Aware Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Modern large language models (LLMs) increasingly rely on inference-time planning and external tools to improve reasoning. We benchmark this behavior on two real-world settings: event-centric question answering over graph-structured knowledge (Event-QA) and persuasive response generation in Reddit ChangeMyView (CMV). |
Subha Ghoshal; Ali Al-Bustami; | arxiv-cs.CL | 2026-01-05 |
| 828 | Adversarial Question Answering Robustness: A Multi-Level Error Analysis and Mitigation Study Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We perform comprehensive multi-level error analysis using five complementary categorization schemes, identifying negation confusion and entity substitution as the primary failure modes. |
Agniv Roy Choudhury; Vignesh Ponselvan Rajasingh; | arxiv-cs.CL | 2026-01-05 |
| 829 | MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and are largely not open-ended, given the difficulty of evaluating free-form answers. In this paper, we introduce a novel open-ended multi-modal VideoQA benchmark, MovieRecapsQA created using movie recap videos–a distinctive type of YouTube content that summarizes a film by presenting its key events through synchronized visual (recap video) and textual (recap summary) modalities. |
Shaden Shaar; Bradon Thymes; Sirawut Chaixanien; Claire Cardie; Bharath Hariharan; | arxiv-cs.CV | 2026-01-05 |
| 830 | Augmenting Medical Visual Question Answering with Mixup, Label Smoothing, and Layer-wise Relevance Propagation EXplainable Artificial Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, an imbalance in the number and distribution of image and Question–Answer (QA) pairs poses challenges for developing robust models. This study proposes improving existing MVQA datasets using data augmentation techniques specifically Mixup and Label Smoothing—to address this issue. |
Sheerin Sitara Noor Mohamed; Kavitha Srinivasan; | PeerJ Computer Science | 2026-01-05 |
| 831 | PdfQA: Diverse, Challenging, and Realistic Question Answering Over PDFs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present pdfQA, a multi-domain 2K human-annotated (real-pdfQA) and 2K synthetic dataset (syn-pdfQA) differentiating QA pairs in ten complexity dimensions (e.g., file type, source modality, source position, answer type). |
TOBIAS SCHIMANSKI et. al. | arxiv-cs.CL | 2026-01-05 |
| 832 | DeCode: Decoupling Content and Delivery for Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce DeCode, a training-free, model-agnostic framework that adapts existing LLMs to produce contextualized answers in clinical settings. |
Po-Jen Ko; Chen-Han Tsai; Yu-Shao Peng; | arxiv-cs.CL | 2026-01-05 |
| 833 | CTIS-QA: Clinical Template-Informed Slide-level Question Answering for Pathology Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a clinical diagnosis template-based pipeline to systematically collect and structure pathological information. |
HAO LU et. al. | arxiv-cs.CV | 2026-01-04 |
| 834 | MambaFormer: Token-Level Guided Routing Mixture-of-Experts for Accurate and Efficient Clinical Assistance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The deployment of large language models (LLMs) in real-world clinical applications is constrained by the fundamental trade-off between computational cost and the efficiency of linear-time models. To address this, we propose an LLM-based MambaFormer hybrid Mixture-of-Experts (MoE) framework for efficient medical question-answering (QA) and clinical assistance. |
Hamad Khan; Saddam Hussain Khan; | arxiv-cs.CV | 2026-01-03 |
| 835 | Retrieval-Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Multi-hop question answering (QA) requires systems to iteratively retrieve evidence and reason across multiple hops. While recent RAG and agentic methods report strong results, … |
Yuelyu Ji; Zhuochun Li; Rui Meng; Daqing He; | ArXiv | 2026-01-02 |
| 836 | RAGPPI: Retrieval-Augmented Generation Benchmark for Protein-Protein Interactions in Drug Discovery Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Retrieving the biological impacts of protein-protein interactions (PPIs) is essential for target identification (Target ID) in drug development. Given the vast number of proteins … |
YOUNGSEUNG JEON et. al. | Conference of the European Chapter of the Association for … | 2026-01-01 |
| 837 | Task-Level Instructions Induction for Audio Question Answering from Few Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Po-Chun Chen; Hen-Hsen Huang; Hsin-Hsi Chen; | Conference of the European Chapter of the Association for … | 2026-01-01 |
| 838 | VERICITE: Evaluating Sentence-Level Citation Faithfulness in Retrieval-Augmented Medical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yixian Ma; Bohao Chu; Norbert Fuhr; | BioNLP@ACL | 2026-01-01 |
| 839 | Evaluating Large Vision Language Models on Bangla Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
RAFID AHMED et. al. | Annual Meeting of the Association for Computational … | 2026-01-01 |
| 840 | Commonsense Question Answering in Arabic: A Survey of Models, Challenges, and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
K. Amraoui; Yassen Saoudi; M. Gammoudi; | Expert Syst. Appl. | 2026-01-01 |
| 841 | DoKE: Domain Knowledge-enhanced Multimodal Large Language Model-based Framework for Chest X-ray Follow-up Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Zhichuan Wang; Qiao Deng; Tiffany So; Wan Hang Keith Chiu; Edward S. Hui; | Biomed. Signal Process. Control. | 2026-01-01 |
| 842 | Domain-Adapted Retrieval-Augmented Generation for Technical Documentation: Enhancing Reliability and Faithfulness in Technical QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: This paper introduces a domain-adapted Retrieval-Augmented Generation (RAG) pipeline specifically designed for technical question answering within industrial documentation. To … |
Veronika Krainovskikh; Timur Samigulin; | IEEE Access | 2026-01-01 |
| 843 | Dual-space Intervention for Mitigating Bias in Robust Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
RUNMIN WANG et. al. | Expert Syst. Appl. | 2026-01-01 |
| 844 | Question Understanding and Temporality Guiding for Video Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Video Question Answering (VideoQA) aims to answer a question based on the content of a given video. Recent methods adapt image-text pre-trained models to the VideoQA task by … |
Jiahao Lu; Si-Si You; Bing-Kun Bao; | IEEE Transactions on Multimedia | 2026-01-01 |
| 845 | TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Mohammadamin Shafiei; Hamidreza Saffari; Mohammad Taher Pilehvar; Alessandro Raganato; | Conference of the European Chapter of the Association for … | 2026-01-01 |
| 846 | Combining Retrieved with Generated Contexts Via A Listwise Reranker for Open-domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yi Zhao; Yanxiang He; Xiaotong Zhang; Weidong Wen; | Neurocomputing | 2026-01-01 |
| 847 | NII-UIT at VBS2026: Towards Effective Visual Question Answering for Interactive and Multimodal Video Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Bao Tran; Tien Do; T. Ngo; Duy-Dinh Le; Shin’ichi Satoh; | Conference on Multimedia Modeling | 2026-01-01 |
| 848 | KGMem-RAG: Knowledge Graph Enhanced Memory Retrieval-Augmented Generation for Educational Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Yilan Gu; Jiali Bian; | International Conference on Intelligent Computing | 2026-01-01 |
| 849 | The Problem of Ambiguity in Table Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Jorge Osés Grijalba; L. Urena-Lopez; Eugenio Martínez Cámara; J. Camacho-Collados; | Conference of the European Chapter of the Association for … | 2026-01-01 |
| 850 | NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
VISHAL PRAMANIK et. al. | Annual Meeting of the Association for Computational … | 2026-01-01 |
| 851 | RC-RAG: A Reliability-Centered Retrieval-Augmented LLM Framework for PHM Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Large language model-based question answering has the potential to improve access to technical knowledge, but hallucinated responses remain a severe reliability risk when answers … |
Han-Qi Su; Ruoxin Wang; Dai-Yan Ji; Jay Lee; | IEEE Transactions on Reliability | 2026-01-01 |
| 852 | H2G-RAG: A Hierarchical Hybrid Graph Retrieval Framework for Regulatory Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Shaocong Xiong; Gege Qi; He Li; Guang-Jun Shi; | International Conference on Intelligent Computing | 2026-01-01 |
| 853 | MAP-GR: Medical Aware Prompt and Graph-guided Reasoning for Enhanced Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
YUHAI YU et. al. | Neurocomputing | 2026-01-01 |
| 854 | GlintLM: Graph-Layered Integration with Nodal Topology with Language Models – A Bipartite Approach to Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Zhuofan Chen; Yao Hui Hoon; Renne Ye Kai Ong; J. Wong; | Inf. Syst. | 2026-01-01 |
| 855 | Visual-Linguistic Abductive Reasoning with LLMs for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Jieun Kim; Yujin Jeong; Sung-Bae Cho; | Conference of the European Chapter of the Association for … | 2026-01-01 |
| 856 | UQA-RAG: Enhancing Urdu Question-Answer Retrieval With Semantic Similarity and LLMs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Urdu Question Answering (QA) systems struggle with limited annotated resources and linguistic complexities. These are significant hurdles for traditional Large Language Models … |
T. Khan; Muhammad Arslan; Fahad Alturise; Ahmed H. Almutairi; M. Raza; | IEEE Access | 2026-01-01 |
| 857 | HausaNLP at SemEval-2026 Task 7: Prompt-based Hausa Cultural Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
F. Adam; L. Aliyu; Sani Aji; Abdulhamid Abubakar; A. Shuaibu; | SemEval@ACL | 2026-01-01 |
| 858 | BioASQ at CLEF2026: The Fourteenth Edition of The Large-Scale Biomedical Semantic Indexing and Question Answering Challenge Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
A. NENTIDIS et. al. | European Conference on Information Retrieval | 2026-01-01 |
| 859 | Securely Answering Multi-Hop Questions on The Joint of Private and Public Knowledge Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Knowledge Graph Question Answering (KGQA) plays an important role in modern information service systems. However, due to the challenges in constructing Knowledge Graphs (KGs) and … |
SHUAIPENG LI et. al. | IEEE Transactions on Automation Science and Engineering | 2026-01-01 |
| 860 | Semantic Event Graphs for Long-Form Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Semantic Event Graphs (SEG), a lightweight symbolic interface between video and language that replaces raw frames with compact temporal interaction logs. |
Aradhya Dixit; Tianxi Liang; | arxiv-cs.CV | 2026-01-01 |
| 861 | ESG-QA: Building A Dataset for Question Answering on Environmental, Social, and Governance Pillars Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
GABRIEL ASSIS et. al. | International Conference on Language Resources and … | 2026-01-01 |
| 862 | Retrieval–Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This survey takes the execution procedure as the unit of analysis and introduces a four-axis framework covering (A) overall execution plan, (B) index structure, (C) next-step control (strategies and triggers), and (D) stop/continue criteria. |
Yuelyu Ji; Zhuochun Li; Rui Meng; Daqing He; | arxiv-cs.CL | 2026-01-01 |
| 863 | Enhancing The QA Model Through A Multi-domain Debiasing Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By identifying errors related to lexical bias, numerical reasoning, and entity recognition, we develop a multi-domain debiasing framework incorporating knowledge distillation, debiasing techniques, and domain expansion. |
Yuefeng Wang; ChangJae Lee; | arxiv-cs.CL | 2026-01-01 |
| 864 | Text-Attributed Graph Augmented Large Language Models for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Zhong-Kun Zhu; Junwei Zhang; Dongjin Yu; Xiaolin Li; | Knowledge Science, Engineering and Management | 2026-01-01 |
| 865 | Explicit Abstention Knobs for Predictable Reliability in Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate whether confidence-based abstention provides reliable control over error rates in video question answering, and whether that control remains robust under distribution shift. |
Jorge Ortiz; | arxiv-cs.AI | 2025-12-31 |
| 866 | Intelligent Diabetes Question Answering System Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
步翔 徐; | Artificial Intelligence and Robotics Research | 2025-12-31 |
| 867 | DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patient-authored queries and clinical context, limiting their applicability to patient-centered care. To address this gap, we introduce DermaVQA-DAS, an extension of the DermaVQA dataset that supports two complementary tasks: closed-ended question answering (QA) and dermatological lesion segmentation. |
WEN-WAI YIM et. al. | arxiv-cs.CV | 2025-12-30 |
| 868 | HaluNet: Multi-Granular Uncertainty Modeling for Efficient Hallucination Detection in LLM Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present \textbf{HaluNet}, a lightweight and trainable neural framework that integrates multi granular token level uncertainties by combining semantic embeddings with probabilistic confidence and distributional uncertainty. |
CHAODONG TONG et. al. | arxiv-cs.CL | 2025-12-30 |
| 869 | Comparative Analysis of The Risk of Hadith Errors in Question-Answering Systems Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study aims to conduct a comparative analysis of the risk of errors generated by Large Language Model-based Question-Answering systems in answering hadith-related questions. |
Hakkun Elmunsyah; | International Journal of Research and Scientific Innovation | 2025-12-30 |
| 870 | EdgeJury: Cross-Reviewed Small-Model Ensembles for Truthful Question Answering on Serverless Edge Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present EdgeJury, a lightweight ensemble framework that improves truthfulness and robustness using only small instruction-tuned language models (3B-8B) suitable for serverless edge inference. |
Aayush Kumar; | arxiv-cs.LG | 2025-12-29 |
| 871 | Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through comprehensive ablation experiments and error analysis, we find that domain-specific training with the SecBERT encoder significantly contributes to our best neural symbolic model surpassing the FinQA paper’s top model, which serves as our baseline. |
Yukun Zhang; Stefan Elbl Droguett; Samyak Jain; | arxiv-cs.CL | 2025-12-29 |
| 872 | Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We investigate the use of LLMs for retrieval augmented question answering. |
Dingmin Wang; Ji Ma; Shankar Kumar; | arxiv-cs.CL | 2025-12-29 |
| 873 | Chain-of-thought Reviewing and Correction for Time Series Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Different from purely textual tasks, time series data are inherently verifiable, enabling consistency checking between reasoning steps and the original input. Motivated by this property, we propose T3LLM, which performs multi-step reasoning with an explicit correction mechanism for time series question answering. |
Chen Su; Yuanhe Tian; Yan Song; | arxiv-cs.CL | 2025-12-27 |
| 874 | Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a demonstration of uncertainty-aware dynamic KGs, a framework that combines (i) dynamic construction of evolving KGs, (ii) confidence scoring and uncertainty-aware retrieval, and (iii) an interactive interface for reliable and interpretable QA. |
YU TAKAHASHI et. al. | arxiv-cs.CL | 2025-12-26 |
| 875 | KG20C & KG20C-QA: Scholarly Knowledge Graph Benchmarks for Link Prediction and Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present KG20C and KG20C-QA, two curated datasets for advancing question answering (QA) research on scholarly data. |
Hung-Nghiep Tran; Atsuhiro Takasu; | arxiv-cs.IR | 2025-12-25 |
| 876 | Medical QA Dialogue Datasets in RAG Systems Performance Evaluation and ChatGPT Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Using ChatGPT-3.5 as a baseline and extending to GPT-4o and GPT-5, we compare multiple retrieval pipelines, including dense retrieval, Cross-Encoder reranking, Reciprocal Rank Fusion (RRF), and Cascade RRF→Rerank. |
Muretijiang Muhetaer; Ailimulati Yusupu; Wang Yifan; Munire Mutalipu; Fan Hao; | Scientific Reports | 2025-12-24 |
| 877 | MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, continual reflections of the same LLM onto itself exhibit degeneration of thought, where the LLM continues to repeat the same errors again and again even with the knowledge that its wrong. To address this problem, we instead introduce multi-agent with multi-persona debators as the method to generate reflections. |
ONAT OZER et. al. | arxiv-cs.AI | 2025-12-23 |
| 878 | CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce CycleChart, a consistency-based learning framework for bidirectional chart understanding and generation. |
Dazhen Deng; Sen Yang; Yuchen He; Yuan Tian; Yingcai Wu; | arxiv-cs.CL | 2025-12-22 |
| 879 | Simulated Patient Systems Powered By Large Language Model-based AI Agents Offer Potential for Transforming Medical Education IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A user study with medical students shows that AIPatient delivers high fidelity, usability, and educational value, matching or exceeding human-simulated patients in history-taking. |
HUIZI YU et. al. | Communications Medicine | 2025-12-19 |
| 880 | Toward Ethical AI Through Bayesian Uncertainty in Neural Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We explore Bayesian reasoning as a means to quantify uncertainty in neural networks for question answering. |
Riccardo Di Sipio; | arxiv-cs.CL | 2025-12-19 |
| 881 | DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Methods: We benchmarked eleven existing LLMs with varying parameter sizes (8 billion to 70+ billion) using a 141-question pharmacy dataset. |
HOUMAN KAZEMZADEH et. al. | arxiv-cs.CL | 2025-12-16 |
| 882 | Evaluating The Capability of Video Question Generation for Expert Knowledge Elicitation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For a continuous improvement of VQG models, we propose a protocol that evaluates the ability by simulating question-answering communication with experts using a question-to-answer retrieval. |
Huaying Zhang; Atsushi Hashimoto; Tosho Hirasawa; | arxiv-cs.CV | 2025-12-16 |
| 883 | Code-Driven LLM Agent for One-Shot Explanatory Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose OneCoLA (One-shot and training-free Code-driven LLM Agent), a novel framework for Multimodal Explanatory Visual Question Answering (MEVQA). |
Zuyi Zhou; Dizhan Xue; Baoyuan Qi; Shengsheng Qian; Changsheng Xu; | ACM Transactions on Multimedia Computing, Communications, … | 2025-12-16 |
| 884 | Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we contribute to reducing the multimodal NLP resource gap for Romanian. |
George-Andrei Dima; Dumitru-Clementin Cercel; | arxiv-cs.CL | 2025-12-16 |
| 885 | Improving VQA Reliability: A Dual-Assessment Approach with Self-Reflection and Cross-Model Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the susceptibility of VLMs to hallucinations can lead to overconfident yet incorrect answers, severely undermining answer reliability. To address this, we propose Dual-Assessment for VLM Reliability (DAVR), a novel framework that integrates Self-Reflection and Cross-Model Verification for comprehensive uncertainty estimation. |
XIXIAN WU et. al. | arxiv-cs.CV | 2025-12-16 |
| 886 | Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Visually Grounded Active View Selection (VG-AVS), a task that selects the most informative next viewpoint using only the visual information in the current image, without relying on scene memory or external knowledge. |
Juil Koo; Daehyeon Choi; Sangwoo Youn; Phillip Y. Lee; Minhyuk Sung; | arxiv-cs.CV | 2025-12-15 |
| 887 | KFS-Bench: Comprehensive Evaluation of Key Frame Sampling in Long Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose KFS-Bench, the first benchmark for key frame sampling in long video question answering (QA), featuring multi-scene annotations to enable direct and robust evaluation of sampling strategies. |
Zongyao Li; Kengo Ishida; Satoshi Yamazaki; Xiaotong Ji; Jianquan Liu; | arxiv-cs.CV | 2025-12-15 |
| 888 | TRUSS-Med: Medical Question Answering Via Transformers, Retrieval, and Unified State Space Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: The advent of Large Language Models (LLMs) and other advances in Artificial Intelligence (AI) has revolutionized natural language processing across a wide range of domains. … |
Xiang Zhu; Mustaqeem Khan; Eya Mhedhbi; Yu Liang; A. Othmani; | 2025 IEEE International Conference on Bioinformatics and … | 2025-12-15 |
| 889 | Hybrid Retrieval-Augmented Generation for Robust Multilingual Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We develop and evaluate a multilingual Retrieval-Augmented Generation pipeline specifically designed for question answering on noisy historical documents. |
Anthony Mudet; Souhail Bakkali; | arxiv-cs.DL | 2025-12-14 |
| 890 | Time-Aware Complex Question Answering Over Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Luyi Bai; Tongyue Zhang; Guangchen Feng; | Data Knowl. Eng. | |
| 891 | Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking By Contrasting Layers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing RAG methods for simple and multi-hop question answering (QA) are still prone to incorrect retrievals and hallucinations. To address these limitations, we propose CoopRAG, a novel RAG framework for the question answering task in which a retriever and an LLM work cooperatively with each other by exchanging informative knowledge, and the earlier and later layers of the retriever model work cooperatively with each other to accurately rank the retrieved documents relevant to a given query. |
Youmin Ko; Sungjong Seo; Hyunjoon Kim; | arxiv-cs.CL | 2025-12-11 |
| 892 | MedBioRAG: Semantic Search and Retrieval-Augmented Generation with Large Language Models for Medical and Biological QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce MedBioRAG, a retrieval-augmented model designed to improve biomedical QA performance through a combination of semantic and lexical search, document retrieval, and supervised fine-tuning. |
Seonok Kim; | arxiv-cs.CL | 2025-12-10 |
| 893 | AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Grounding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Here, we introduce AutoMedic, a multi-agent simulation framework that enables automated evaluation of LLMs as clinical conversational agents. |
Gyutaek Oh; Sangjoon Park; Byung-Hoon Kim; | arxiv-cs.CL | 2025-12-10 |
| 894 | DIANGPT: A Review on A Domain-Specific Question Answering System Using LORA and RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This review evaluates DianGPT’s architectural pipeline, including dataset processing, supervised fine-tuning, retrieval workflows, and automated evaluation strate- gies. |
Pallavi SK; Pradeep Nayak; Nischitha Nischitha; Omkar KS; Omkar JK; | International Journal of Scientific Research in Engineering … | 2025-12-10 |
| 895 | DBLP QuAD 2.0: Scholarly Natural Questions from SPARQL Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: We present DBLP-QuAD 2.0, designed to evaluate Scholarly Knowledge Graph Question Answering (KGQA) over DBLP. Recent updates in the underlying DBLP KG, including new entities and … |
TILAHUN ABEDISSA TAFFA et. al. | Proceedings of the 13th Knowledge Capture Conference 2025 | 2025-12-10 |
| 896 | NeuReg: Neuro-Symbolic QA Generation from Regulatory Compliance Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Education providers face increasing challenges in complying with complex and evolving funding regulations. While large language models (LLMs) have the potential to support … |
Umair Arshad; D. Corsar; I. Nkisi-Orji; | Proceedings of the 13th Knowledge Capture Conference 2025 | 2025-12-10 |
| 897 | SimpleDevQA: Benchmarking Large Language Models on Development Knowledge QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through this pipeline, we introduce SimpleDevQA, a multilingual benchmark derived from real user dialogues. |
JING ZHANG et. al. | arxiv-cs.SE | 2025-12-09 |
| 898 | ExpandFuse: A Hybrid Retrieval Framework with Query Expansion and Topic-Aware Reranking for Multi-Hop Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Retrieval-augmented large language models (LLMs) have shown strong performance in open-domain question answering but often struggle with multi-hop reasoning and noisy evidence … |
Keerthana Murugaraj; Salima Lamsiyah; Martin Theobald; | 2025 IEEE International Conference on Big Data (BigData) | 2025-12-08 |
| 899 | BoundingDocs: A Unified Dataset for Document Question Answering with Spatial Annotations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Abstract We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understanding (VRDU). |
Simone Giovannini; Fabio Coppini; Andrea Gemelli; Simone Marinai; | International Journal on Document Analysis and Recognition … | 2025-12-06 |
| 900 | FalseCoTQA: Adversarial Multi-Hop QA Via Knowledge-Grounded False Chains of Thought Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Multi-hop question answering (QA) models excel at decomposing complex queries into sequential reasoning steps, yet they remain vulnerable to subtly flawed inference chains that … |
Julien Serbanescu; Mahdiyar Ali Akbar Alavi; F. Ensan; F. Zarrinkalam; | Proceedings of the 2025 Annual International ACM SIGIR … | 2025-12-06 |
| 901 | Knowing What’s Missing: Assessing Information Sufficiency in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a structured Identify-then-Verify framework for robust sufficiency modeling. |
Akriti Jain; Aparna Garimella; | arxiv-cs.CL | 2025-12-06 |
| 902 | Modeling Contextual Passage Utility for Multihop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a lightweight approach to model contextual passage utility, accounting for inter-passage dependencies. |
Akriti Jain; Aparna Garimella; | arxiv-cs.CL | 2025-12-06 |
| 903 | ArtistMus: A Globally Diverse, Artist-Centric Benchmark for Retrieval-Augmented Music Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce MusWikiDB, a vector database of 3.2M passages from 144K music-related Wikipedia pages, and ArtistMus, a benchmark of 1,000 questions on 500 diverse artists with metadata such as genre, debut year, and topic. |
Daeyong Kwon; SeungHeon Doh; Juhan Nam; | arxiv-cs.CL | 2025-12-05 |
| 904 | Collective Narrative Grounding: Community-Coordinated Data Contributions to Improve Local AI Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large language model (LLM) question-answering systems often fail on community-specific queries, creating knowledge blind spots that marginalize local voices and reinforce epistemic injustice. We present Collective Narrative Grounding, a participatory protocol that transforms community stories into structured narrative units and integrates them into AI systems under community governance. |
Zihan Gao; Mohsin Y. K. Yousufi; Jacob Thebault-Spieker; | arxiv-cs.CL | 2025-12-05 |
| 905 | Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a retrieval-augmented generation (RAG) based medical QA system that combines domain-specific knowledge retrieval with open-source LLMs to answer medical questions. |
TASNIMUL HASSAN et. al. | arxiv-cs.CL | 2025-12-05 |
| 906 | Grounded Multilingual Medical Reasoning for Question Answering with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present a method to generate multilingual reasoning traces grounded in factual medical knowledge. |
Pietro Ferrazzi; Aitor Soroa; Rodrigo Agerri; | arxiv-cs.CL | 2025-12-05 |
| 907 | PathFinder: MCTS and LLM Feedback-based Path Selection for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Hence, we propose PATHFINDER, an approach that: (i) uses Monte Carlo Tree Search to generate training path traces, (ii) improves training data quality by filtering erroneous and lengthy traces using sub-answer recall and LLM-as-a-judge verification, and (iii) reformulates sub-queries to handle failed retrieval cases. |
Durga Prasad Maram; Kalpa Gunaratna; Vijay Srinivasan; Haris Jeelani; Srinivas Chappidi; | arxiv-cs.LG | 2025-12-04 |
| 908 | Fine-Tuning BERT for Domain-Specific Question Answering: Toward Educational NLP Resources at University Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we developed a chatbot for the University of Limerick’s Department of Electronic and Computer Engineering to provide course information to students. |
Aurélie Montfrond; | arxiv-cs.CL | 2025-12-04 |
| 909 | BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing RAG approaches often focus on general documents, and they overlook the fact that many real-world documents (such as books, booklets, handbooks, etc.) have a hierarchical structure, which organizes their content from different granularity levels, leading to poor performance for the QA task. To address these limitations, we introduce BookRAG, a novel RAG approach targeted for documents with a hierarchical structure, which exploits logical hierarchies and traces entity relations to query the highly relevant information. |
Shu Wang; Yingli Zhou; Yixiang Fang; | arxiv-cs.IR | 2025-12-02 |
| 910 | CryptoQA: A Large-scale Question-answering Dataset for AI-assisted Cryptography Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their ability to perform deep reasoning and mathematical analysis, particularly for complex tasks as required in cryptography, remains poorly understood, largely due to the lack of suitable data for evaluation and training. To address this gap, we present CryptoQA, the first large-scale question-answering (QA) dataset specifically designed for cryptography. |
MAYAR ELFARES et. al. | arxiv-cs.CR | 2025-12-02 |
| 911 | ACRA: An Adaptive Chain Retrieval Architecture for Multi-modal Knowledge-Augmented Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
ZIHAO ZHANG et. al. | Knowl. Based Syst. | 2025-12-01 |
| 912 | Retrieving on A Topic Graph for Long Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
SHIWEI CHEN et. al. | Neurocomputing | 2025-12-01 |
| 913 | CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We propose Corpus Retrieval and Augmentation for Fine-Tuning (CRAFT), a method for generating synthetic datasets, given a small number of user-written few-shots that demonstrate the task to be performed. |
Ingo Ziegler; Abdullatif Köksal; Desmond Elliott; Hinrich Schütze; | Transactions of the Association for Computational … | 2025-12-01 |
| 914 | Memory-Augmented Knowledge Fusion with Safety-Aware Decoding for Domain-Adaptive Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce Knowledge-Aware Reasoning and Memory-Augmented Adaptation (KARMA), a novel framework designed to enhance QA performance in care scenarios. |
Lei Fu; Xiang Chen; Kaige Gao Xinyue Huang; Kejian Tong; | arxiv-cs.CL | 2025-12-01 |
| 915 | Memory-enriched Thought-by-thought Framework for Complex Diagram Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
XINYU ZHANG et. al. | Comput. Vis. Image Underst. | 2025-12-01 |
| 916 | CAIRNS: Balancing Readability and Scientific Accuracy in Climate Adaptation Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present Climate Adaptation question-answering with Improved Readability and Noted Sources (CAIRNS), a framework that enables experts — farmer advisors — to obtain credible preliminary answers from complex evidence sources from the web. |
Liangji Kong; Aditya Joshi; Sarvnaz Karimi; | arxiv-cs.CL | 2025-12-01 |
| 917 | Enhancing Question Answering for Low-Resource Languages: The Case of Kazakh Language Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: The Kazakh language, with its agglutinative morphology and scarce annotated data, presents challenges for accurate Question Answering (QA). This study proposes a three-stage … |
A. Tleubayeva; Zhansaya Makhambetova; A. Mansurova; A. Shomanov; | Proceedings of the 18th IEEE/ACM International Conference … | 2025-12-01 |
| 918 | Zero-Shot Knowledge-Based Visual Question Answering with Frozen Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
JING LIU et. al. | Big Data Min. Anal. | 2025-12-01 |
| 919 | Advancing Academic Chatbots: Evaluation of Non Traditional Outputs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We implemented a prototype combining Meta’s LLaMA 3 70B open weight and OpenAI’s GPT 4o mini API based. |
Nicole Favero; Francesca Salute; Daniel Hardt; | arxiv-cs.CL | 2025-11-30 |
| 920 | Tourism Question Answer System in Indian Language Using Domain-Adapted Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, a dataset comprising 7,715 Hindi QA pairs pertaining to Varanasi tourism was constructed and subsequently augmented with 27,455 pairs generated via Llama zero-shot prompting. |
Praveen Gatla; Nikita Kanwar; Gouri Sahoo; Rajesh Kumar Mundotiya; | arxiv-cs.CL | 2025-11-28 |
| 921 | ORCA: Open-ended Response Correctness Assessment for Audio Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present ORCA (Open-ended Response Correctness Assessment), a framework that models the variability in human judgments using Beta distributions to predict both expected correctness and uncertainty. |
ŠIMON SEDLÁČEK et. al. | arxiv-cs.SD | 2025-11-28 |
| 922 | Machine Learning and Deep Learning Techniques in Arabic Question Answering Systems: Innovations and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, we recognize that the impact of AQAS extends beyond academia; it has significant implications for various sectors, including education, technology, and information access. Through this comprehensive examination, we aim to lay the groundwork for ongoing innovation and development in AQAS. |
Azza Mohamed; Khaled Abdelqader; Khaled Shaalan; | PeerJ Computer Science | 2025-11-28 |
| 923 | WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce WearVQA, the first benchmark specifically designed to evaluate the Visual Question Answering (VQA) capabilities of multi-model AI assistant on wearable devices like smart glasses. |
EUN CHANG et. al. | arxiv-cs.AI | 2025-11-27 |
| 924 | Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current solutions typically isolate retrieval methods focusing either on structured data (SQL/Cypher) or unstructured semantic search but fail to integrate both simultaneously. This work presents MediGRAF (Medical Graph Retrieval Augmented Framework), a novel hybrid Graph RAG system that bridges this gap. |
Samuel Thio; Matthew Lewis; Spiros Denaxas; Richard JB Dobson; | arxiv-cs.CL | 2025-11-27 |
| 925 | JBE-QA: Japanese Bar Exam QA Dataset for Assessing Legal Domain Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce JBE-QA, a Japanese Bar Exam Question-Answering dataset to evaluate large language models’ legal knowledge. |
ZHIHAN CAO et. al. | arxiv-cs.CL | 2025-11-27 |
| 926 | KA-RAG: Integrating Knowledge Graphs and Agentic Retrieval-Augmented Generation for An Intelligent Educational Question-Answering Model IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Generative artificial intelligence (AI) and large language models (LLMs) are reshaping the landscape of intelligent educational systems; however, existing solutions often suffer from unstructured resource organization, limited interpretability, and suboptimal retrieval precision. To address these challenges, this study introduces KA-RAG, a course-oriented question answering (QA) framework that integrates a structured Knowledge Graph (KG) with an Agentic Retrieval-Augmented Generation (Agentic-RAG) workflow. |
Fangqun Gao; Shu Xu; Weiyan Hao; Tao Lu; | Applied Sciences | 2025-11-26 |
| 927 | LongVT: Incentivizing Thinking with Long Videos Via Native Tool Calling IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by how humans comprehend long videos – by first skimming globally and then examining relevant clips for details – we introduce LongVT, an end-to-end agentic framework that enables Thinking with Long Videos via interleaved Multimodal Chain-of-Tool-Thought. |
ZUHAO YANG et. al. | arxiv-cs.CV | 2025-11-25 |
| 928 | Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional KGQA systems preserve structure but typically support only single-turn QA, incur high latency, and struggle with coreference and context tracking. To address these limitations, we propose Chatty-KG, a modular multi-agent system for conversational QA over KGs. |
REHAM OMAR et. al. | arxiv-cs.CL | 2025-11-25 |
| 929 | SFA: Scan, Focus, and Amplify Toward Guidance-aware Answering for Video TextVQA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, the model must identify question-relevant textual cues and filter out redundant or irrelevant information to ensure answering is guided by the most relevant and informative cues. To address these challenges, we propose SFA, a training-free framework and the first Video-LLM-based method tailored for Video TextVQA, motivated by the human process of answering questions. |
HAIBIN HE et. al. | arxiv-cs.CV | 2025-11-25 |
| 930 | ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling from egocentric interaction in a visual question answering (VQA) format. |
QINENG WANG et. al. | arxiv-cs.AI | 2025-11-25 |
| 931 | GHR-VQA: Graph-guided Hierarchical Relational Reasoning for Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose GHR-VQA, Graph-guided Hierarchical Relational Reasoning for Video Question Answering (Video QA), a novel human-centric framework that incorporates scene graphs to capture intricate human-object interactions within video sequences. |
Dionysia Danai Brilli; Dimitrios Mallis; Vassilis Pitsikalis; Petros Maragos; | arxiv-cs.CV | 2025-11-25 |
| 932 | VeriSciQA: An Auto-Verified Dataset for Scientific Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent work uses LVLMs to synthesize data at scale, we identify systematic errors in their resulting QA pairs, stemming from LVLMs’ inherent limitations and information asymmetry between figures and text. To address these challenges, we propose a verification-centric Generate-then-Verify framework that first generates QA pairs with figure-associated textual context, then applies cross-modal consistency checks against figures along with auxiliary filters to eliminate erroneous pairs. |
Yuyi Li; Daoyuan Chen; Zhen Wang; Yutong Lu; Yaliang Li; | arxiv-cs.CV | 2025-11-24 |
| 933 | EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present EAGER, an edge-aligned defense framework that integrates parameter-efficient quantization with domain-specific preference alignment to jointly optimize efficiency, robustness, and accuracy. |
Onat Gungor; Roshan Sood; Jiasheng Zhou; Tajana Rosing; | arxiv-cs.CR | 2025-11-24 |
| 934 | Vidi2: Large Multimodal Models for Video Understanding and Creation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To enable comprehensive evaluation of STG in practical settings, we introduce a new benchmark, VUE-STG, which offers four key improvements over existing STG datasets: 1) Video duration: spans from roughly 10s to 30 mins, enabling long-context reasoning; 2) Query format: queries are mostly converted into noun phrases while preserving sentence-level expressiveness; 3) Annotation quality: all ground-truth time ranges and bounding boxes are manually annotated with high accuracy; 4) Evaluation metric: a refined vIoU/tIoU/vIoU-Intersection scheme. |
VIDI TEAM et. al. | arxiv-cs.CV | 2025-11-24 |
| 935 | MedPerturbing LLMs: A Comparative Study of Toxicity, Prompt Tuning, and Jailbreaks in Medical QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we evaluate the toxicity of widely used general-purpose LLMs in medical question–answering tasks. |
Arash Asgari; Amirreza Naziri; Laleh Seyyed-Kalantari; | Proceedings of the AAAI Symposium Series | 2025-11-23 |
| 936 | RoadSceneVQA: Benchmarking Visual Question Answering in Roadside Perception Systems for Intelligent Transportation System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Based on the above, we propose the baseline model RoadMind. |
RUNWEI GUAN et. al. | arxiv-cs.CV | 2025-11-22 |
| 937 | Health-oriented Multimodal Food Question Answering with Implicit and Explicit Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save |
Menghao Hu; Y. Song; Xiaoshan Yang; Yaowei Wang; Changsheng Xu; | ACM Transactions on Multimedia Computing, Communications … | 2025-11-22 |
| 938 | Measuring The Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a complementary question: Does lexical training-data coverage of the question and/or generated answer provide additional signal for hallucination detection? |
Shuo Zhang; Fabrizio Gotti; Fengran Mo; Jian-Yun Nie; | arxiv-cs.CL | 2025-11-22 |
| 939 | SMILE: A Composite Lexical-Semantic Metric for Question-Answering Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Model (LLM) based evaluators, though powerful, come with drawbacks like high costs, bias, inconsistency, and hallucinations. To address these issues, we introduce SMILE: Semantic Metric Integrating Lexical Exactness, a novel approach that combines sentence-level semantic understanding with keyword-level semantic understanding and easy keyword matching. |
SHRIKANT KENDRE et. al. | arxiv-cs.CL | 2025-11-21 |
| 940 | HMA: A Hierarchical Multi-Agent System for Document Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Document Question Answering (DocQA) stands as a core task within the domain of natural language processing. Nevertheless, when confronted with complex documents that incorporate … |
Qihang Hou; Guowei Liu; Pinpin Zhu; | Proceedings of the 2025 4th International Conference on … | 2025-11-21 |
| 941 | MGA-VQA: Secure and Interpretable Graph-Augmented Visual Question Answering with Memory-Guided Protection Against Unauthorized Knowledge Use Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose MGA-VQA, a multi-modal framework that integrates token-level encoding, spatial graph reasoning, memory-augmented inference, and question-guided compression. |
Ahmad Mohammadshirazi; Pinaki Prasad Guha Neogi; Dheeraj Kulshrestha; Rajiv Ramnath; | arxiv-cs.CV | 2025-11-21 |
| 942 | EduMod-LLM: A Modular Approach for Designing Flexible and Transparent Educational Assistants Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce {\model}, a modular function-calling LLM pipeline, and present a comprehensive evaluation along three key axes: function calling strategies, retrieval methods, and generative language models. |
Meenakshi Mittal; Rishi Khare; Mihran Miroyan; Chancharik Mitra; Narges Norouzi; | arxiv-cs.CL | 2025-11-21 |
| 943 | Question Answering Models for Information Extraction from Perovskite Materials Science Literature IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we developed and tested a Question Answering (QA) approach to extract material-property relationships from scientific publications. |
Matilda Sipilä; Farrokh Mehryary; Sampo Pyysalo; Filip Ginter; Milica Todorović; | Communications Materials | 2025-11-20 |
| 944 | ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present ESGBench, a benchmark dataset and evaluation framework designed to assess explainable ESG question answering systems using corporate sustainability reports. |
Sherine George; Nithish Saji; | arxiv-cs.CL | 2025-11-20 |
| 945 | FlipVQA-Miner: Cross-Page Visual Question-Answer Mining from Textbooks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose an automated pipeline that extracts well-formed QA and visual-QA (VQA) pairs from educational documents by combining layout-aware OCR with LLM-based semantic parsing. |
ZHEN HAO WONG et. al. | arxiv-cs.AI | 2025-11-20 |
| 946 | AVATAAR: Agentic Video Answering Via Temporal Adaptive Alignment and Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although large vision language models (LVLMs) have enhanced performance, they often face challenges with nuanced queries that demand both a comprehensive understanding and detailed analysis. To overcome these obstacles, we introduce AVATAAR, a modular and interpretable framework that combines global and local video context, along with a Pre Retrieval Thinking Agent and a Rethink Module. |
Urjitkumar Patel; Fang-Chun Yeh; Chinmay Gondhalekar; | arxiv-cs.CV | 2025-11-19 |
| 947 | HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current multilingual VLM evaluations suffer from four major limitations: reliance on unverified auto-translations, narrow task/domain coverage, limited sample sizes, and lack of cultural and natively sourced Question-Answering (QA). To address these gaps, we present a scalable framework to evaluate VLMs in Indian languages and compare it with performance in English. |
RISHIKANT CHIGRUPAATII et. al. | arxiv-cs.CL | 2025-11-19 |
| 948 | Development of An Intelligent Information Retrieval System Based on Ontology, Linguistic Algorithms and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study proposes a hybrid semantic question-answering (QA) system for the Kazakh language that integrates ontological modeling, linguistic processing, and large language models (LLMs). |
Assel Mukanova; Aizhan Nazyrova; Altanbek Zulkhazhav; Zhanar Lamasheva; Assem Dauletkaliyeva; | Applied Sciences | 2025-11-19 |
| 949 | BBox DocVQA: A Large Scale Bounding Box Grounded Dataset for Enhancing Reasoning in Document Visual Question Answer Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing DocVQA datasets are limited to the page level and lack fine grained spatial grounding, constraining the interpretability and reasoning capability of Vision Language Models (VLMs). To address this gap, we introduce BBox DocVQA a large scale, bounding box grounded dataset designed to enhance spatial reasoning and evidence localization in visual documents. |
WENHAN YU et. al. | arxiv-cs.DB | 2025-11-18 |
| 950 | Audio Question Answering with GRPO-Based Fine-Tuning and Calibrated Segment-Level Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this report, we describe our submission to Track 5 of the DCASE 2025 Challenge for the task of Audio Question Answering(AQA). |
MARCEL GIBIER et. al. | arxiv-cs.SD | 2025-11-18 |
| 951 | Multi Table QA:Evaluating Modern LLM Strategies on Table0 Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce tool-augmented reasoning as a paradigm for multi-table QA, and systematically study two complementary strategies: (1) free-form tool interaction, where models iteratively call exploration and computation tools, and(2) structured agent workflows, which stage tool-use into exploration, preparation, and analysis phases. |
Letian Li; | Computers and Artificial Intelligence | 2025-11-18 |
| 952 | Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, its reliance on a proprietary model limits scalability, increases operational costs, and raises concerns about data privacy and generalization. In this work, we revisit and reproduce GeneGPT in a pilot study using open source models, including Llama 3.1, Qwen2.5, and Qwen2.5 Coder, within a monolithic architecture; this allows us to identify the limitations of this approach. |
Haodong Chen; Guido Zuccon; Teerapong Leelanupab; | arxiv-cs.AI | 2025-11-18 |
| 953 | Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA) Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this article, we provide the dataset itself along with the Python scripts used to create it, which can be used to generate additional data of the same kind. |
NIKOS THEODORIDIS et. al. | arxiv-cs.CV | 2025-11-17 |
| 954 | Collaborative QA Using Interacting LLMs. Impact of Network Structure, Node Capability and Distributed Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we model and analyze how a network of interacting LLMs performs collaborative question-answering (CQA) in order to estimate a ground truth given a distributed set of documents. |
Adit Jain; Vikram Krishnamurthy; Yiming Zhang; | arxiv-cs.AI | 2025-11-17 |
| 955 | QA-Noun: Representing Nominal Semantics Via Natural Language Question-Answer Pairs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce QA-Noun, a QA-based framework for capturing noun-centered semantic relations. |
Maria Tseytlin; Paul Roit; Omri Abend; Ido Dagan; Ayal Klein; | arxiv-cs.CL | 2025-11-16 |
| 956 | A Role-Aware Multi-Agent Framework for Financial Education QA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning … |
Andy Zhu; Yingjun Du; | Proceedings of the 6th ACM International Conference on AI … | 2025-11-14 |
| 957 | Local Hybrid Retrieval-Augmented Document QA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Organizations handling sensitive documents face a critical dilemma: adopt cloud-based AI systems that offer powerful question-answering capabilities but compromise data privacy, or maintain local processing that ensures security but delivers poor accuracy. We present a question-answering system that resolves this trade-off by combining semantic understanding with keyword precision, operating entirely on local infrastructure without internet access. |
Paolo Astrino; | arxiv-cs.CL | 2025-11-13 |
| 958 | SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While prior work has mainly focused on improving SQL generation accuracy or filtering questions before execution, there is a lack of a unified benchmark for evaluating independent post-hoc verification mechanisms (i.e., a component that inspects and validates the generated SQL before execution), which is crucial for safe deployment. To fill this gap, we introduce SCARE, a benchmark for evaluating methods that function as a post-hoc safety layer in EHR QA systems. |
Gyubok Lee; Woosog Chay; Edward Choi; | arxiv-cs.CL | 2025-11-13 |
| 959 | PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present the framework PustakAI\footnote{Pustak means `book’ in many Indian languages.} |
Shivam Sharma; Riya Naik; Tejas Gawas; Heramb Patil; Kunal Korgaonkar; | arxiv-cs.CL | 2025-11-13 |
| 960 | Answering Students’ Questions on Course Forums Using Multiple Chain-of-Thought Reasoning and Finetuning RAG-Enabled LLM Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we experiment fine-tuned LLM with RAG method on the HotpotQA dataset. |
Neo Wang; Sonit Singh; | arxiv-cs.CL | 2025-11-12 |
| 961 | Limitations of Large Language Models in Clinical Problem-solving Arising from Inflexible Reasoning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To probe potential LLM failure modes in clinical problem-solving, we present the medical abstraction and reasoning corpus (mARC-QA). |
JONATHAN KIM et. al. | Scientific Reports | 2025-11-11 |
| 962 | Testing Question Answering Software with Context-Driven Question Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce CQ^2A, a context-driven question generation approach for testing question-answering systems. |
SHUANG LIU et. al. | arxiv-cs.SE | 2025-11-11 |
| 963 | C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing hallucination benchmarks (especially in Chinese language) rely on human annotations, making automatical and cost-effective hallucination evaluation challenging. To address this, we introduce HaluAgent, an agentic framework that automatically constructs fine-grained question-answering (QA) dataset based on some knowledge documents. |
XU ZHANG et. al. | cikm | 2025-11-10 |
| 964 | VQA-Induct: Instruction Induction for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current approaches for enhancing VQA reasoning performance often assume access to extensive resources such as large annotated datasets, external tools, or numerous demonstrations, which are impractical for real-world users who typically possess only a few demonstrations. We present VQA-Induct, a framework for data-scarce scenarios that leverages MLLMs’ instruction induction capabilities to induce reusable, purely textual task-level instructions from as few as three demonstrations of the same task, then applies these instructions to new instances using only their image-question pairs. |
Po-Chun Chen; Hen-Hsen Huang; Hsin-Hsi Chen; | cikm | 2025-11-10 |
| 965 | Disentangling Complex Questions in LLMs Via Multi-Hop Dependency Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a novel prompt approach for multi-hop QA viz., MoDeGraph (Multi-Hop Dependency Graphs), that is designed to steer LLMs to extract and model entity relationships in complex questions. |
ROLAND ORUCHE et. al. | cikm | 2025-11-10 |
| 966 | SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). |
Ines Besrour; Jingbo He; Tobias Schreieder; Michael F\{a}rber; | cikm | 2025-11-10 |
| 967 | A Pivot-Enhanced Question Answering Framework: Using Iterative Sub-Question Decomposition and Answer-to-Question Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Question and Answering(QA) in low-resource languages remains a significant challenge due to the scarcity of high-quality training data. To address this, we propose a robust framework for low-resource QA. |
Seyeon Park; Beakcheol Jang; | cikm | 2025-11-10 |
| 968 | Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we conduct a comprehensive analysis of how OCR-induced noise affects the performance of Multilingual QA Systems. |
Bhawna Piryani; Jamshid Mozafari; Abdelrahman Abdallah; Antoine Doucet; Adam Jatowt; | cikm | 2025-11-10 |
| 969 | PEQQS: A Dataset for Probing Extractive Quantity-focused Question Answering from Scientific Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present how our dataset can be used both for the evaluation of extractive quantity-focused QA from science literature and for exploring the impact of search on the downstream results, specifically focusing on hallucinations resulting from processing non-relevant documents with LLMs. |
Maciej Rybinski; Necva B\{o}l\{u}c\{u}; Huichen Yang; Stephen Wan; | cikm | 2025-11-10 |
| 970 | NLP-QA: A Large-scale Benchmark for Informative Question Answering Over Natural Language Processing Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, scholarly QA development is hindered by the scarcity of large-scale, expertly-annotated datasets, that are needed for modern deep learning models. To address this gap and advance scholarly QA, we introduce NLP-QA, a new dataset of question-answer pairs derived from NLP research documents. |
Avishek Lahiri; Debarshi Kumar Sanyal; Imon Mukherjee; | cikm | 2025-11-10 |
| 971 | Revisiting NLI: Towards Cost-Effective and Human-Aligned Metrics for Evaluating LLMs in Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We re-evaluate a lightweight alternative — off-the-shelf Natural Language Inference (NLI) scoring augmented by a simple lexical-match flag and find that this decades-old technique matches GPT-4o’s accuracy (89.9%) on long-form QA, while requiring orders-of-magnitude fewer parameters. To test human alignment of these metrics rigorously, we introduce DIVER-QA, a new 3000-sample human-annotated benchmark spanning five QA datasets and five candidate LLMs. |
Sai Shridhar Balamurali; Lu Cheng; | arxiv-cs.CL | 2025-11-10 |
| 972 | Structuring Video Semantics with Temporal Triplets for Zero-Shot Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a structured representation based on temporal triplets to address two major challenges in traditional approaches: temporal fragmentation and entity reference ambiguity. |
LINLIN ZONG et. al. | cikm | 2025-11-10 |
| 973 | Reference-Aligned Retrieval-Augmented Question Answering Over Heterogeneous Proprietary Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose a RAG-QA framework for internal enterprise use, consisting of: (1) a data pipeline that converts raw multi-modal documents into a structured corpus and QA pairs, (2) a fully on-premise, privacy-preserving architecture, and (3) a lightweight reference matcher that links answer segments to supporting content. |
NAYOUNG CHOI et. al. | cikm | 2025-11-10 |
| 974 | Company-Specific Knowledge Matters: Retrieval-Augmented Generation for Earnings Call Answer Rehearsal Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper explores how to better support corporate executives in answering questions from professional analysts during earnings calls. |
Yung-Yu Shih; Yun-Nung Chen; Chung-Chi Chen; | cikm | 2025-11-10 |
| 975 | QueryBridge: One Million Annotated Questions with SPARQL Queries – Dataset for Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmark datasets (e.g., QALD, LC-QuAD) are limited in size and annotation, hindering QAKG model generalization. To address this, we present QueryBridge, a dataset with over one million annotated questions paired with SPARQL queries. |
Abdelghny Orogat; Ahmed El-Roby; | cikm | 2025-11-10 |
| 976 | BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization Via Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: BookAsSumQA automatically generates aspect-specific QA pairsfrom a narrative knowledge graph to evaluate summary quality based on itsquestion-answering performance. Our experiments using BookAsSumQA revealed thatwhile LLM-based approaches showed higher accuracy on shorter texts, RAG-basedmethods become more effective as document length increases, making them moreefficient and practical for aspect-based book summarization. |
Ryuhei Miyazato; Ting-Ruen Wei; Xuyang Wu; Hsin-Tai Wu; Kei Harada; | arxiv-cs.CL | 2025-11-08 |
| 977 | Efficient Semantic Uncertainty Quantification in Language Models Via Diversity-steered Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a **diversity-steered sampler** that discourages semantically redundant outputs during decoding, covers both autoregressive and masked diffusion paradigms, and yields substantial sample-efficiency gains. |
Ji Won Park; Kyunghyun Cho; | nips | 2025-11-07 |
| 978 | MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles By Reasoning Over Multi-Video Haystacks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing benchmarks for video question answering remain limited in scope, typically involving one clip per query, which falls short of representing the challenges of large-scale, audiovisual retrieval and reasoning encountered in practical applications. To bridge this gap, we introduce a novel task named AVHaystacksQA, where the goal is to identify salient segments across different videos in response to a query and link them together to generate the most informative answer. |
SANJOY CHOWDHURY et. al. | nips | 2025-11-07 |
| 979 | PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts Into Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating either matrix decomposition or mixture-of-experts (MoE) individually decreases performance across tasks, though decomposition improves results on specific domains despite reducing parameters, while MoE increases parameter count without corresponding decrease in training efficiency. Motivated by these observations and the modular nature of PT, we propose PT-MoE, a novel framework that integrates matrix decomposition with MoE routing for efficient PT. |
Zongqian Li; Yixuan Su; Nigel Collier; | nips | 2025-11-07 |
| 980 | Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking By Contrasting Layers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing RAG methods for simple and multi-hop question answering (QA) are still prone to incorrect retrievals and hallucinations. To address these limitations, we propose CoopRAG, a novel RAG framework for the question answering task in which a retriever and an LLM work cooperatively with each other by exchanging informative knowledge, and the earlier and later layers of the retriever model work cooperatively with each other to accurately rank the retrieved documents relevant to a given query. |
Youmin Ko; Sung Jong Seo; Hyunjoon Kim; | nips | 2025-11-07 |
| 981 | QuAnTS: Question Answering on Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We verify that the large-scale QuAnTS dataset iswell-formed and comprehensive through extensive experiments. |
FELIX DIVO et. al. | arxiv-cs.LG | 2025-11-07 |
| 982 | DSAS: A Universal Plug-and-Play Framework for Attention Optimization in Multi-Document Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current solutions either truncate global dependencies or demand costly finetuning, ultimately lacking a universal and simple solution for these challenges. To resolve these limitations, we propose Dual-Stage Adaptive Sharpening (DSAS) containing two modules. |
JIAKAI LI et. al. | nips | 2025-11-07 |
| 983 | Thinker: Learning to Think Fast and Slow IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this search behavior is often imprecise and lacks confidence, resulting in long, redundant responses and highlighting deficiencies in intuition and verification. Inspired by the Dual Process Theory in psychology, we introduce a simple modification to the QA task that includes four stages: Fast Thinking, where the LLM must answer within a strict token budget; Verification, where the model evaluates its initial response; Slow Thinking, where it refines the initial response with more deliberation; and Summarization, where it distills the refinement from the previous stage into precise steps. |
Stephen Chung; Wenyu Du; Jie Fu; | nips | 2025-11-07 |
| 984 | Attack Via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we demonstrate that LLMs can be jailbroken by fine-tuning with only 10 benign QA pairs; our attack exploits the increased sensitivity of LLMs to fine-tuning data after being overfitted. |
Zhixin Xie; Xurui Song; Jun Luo; | nips | 2025-11-07 |
| 985 | Improving Retrieval-Augmented Generation Through Multi-Agent Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Although recent efforts have explored using reinforcement learning (RL) to optimize specific RAG components, these approaches often focus on simple pipelines with only two components or do not adequately address the complex interdependencies and collaborative interactions among the modules. To overcome these limitations, we propose treating the complex RAG pipeline with multiple components as a multi-agent cooperative task, in which each component can be regarded as an RL agent. |
YIQUN CHEN et. al. | nips | 2025-11-07 |
| 986 | IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Vision-language models (VLMs) have demonstrated impressive generalizationacross multimodal tasks, yet most evaluation benchmarks remain Western-centric,leaving open questions about their performance in culturally diverse andmultilingual settings. To address this gap, we introduce IndicVisionBench, thefirst large-scale benchmark centered on the Indian subcontinent. |
ALI FARAZ et. al. | arxiv-cs.CV | 2025-11-06 |
| 987 | Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We apply SynthKGQA to Wikidata to generate GTSQA, a new datasetdesigned to test zero-shot generalization abilities of KG retrievers withrespect to unseen graph structures and relation types, and benchmark popularsolutions for KG-augmented LLMs on it. |
Alberto Cattaneo; Carlo Luschi; Daniel Justus; | arxiv-cs.LG | 2025-11-06 |
| 988 | BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces BanglaMedQA andBanglaMMedBench, the first large-scale Bangla biomedical Multiple ChoiceQuestion (MCQ) datasets designed to evaluate reasoning and retrieval in medicalartificial intelligence (AI). |
Sadia Sultana; Saiyma Sittul Muna; Mosammat Zannatul Samarukh; Ajwad Abrar; Tareque Mohmud Chowdhury; | arxiv-cs.CL | 2025-11-06 |
| 989 | The Illusion of Certainty: Uncertainty Quantification for LLMs Fails Under Ambiguity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While real-world language is inherentlyambiguous, reflecting aleatoric uncertainty, existing UQ methods are typicallybenchmarked against tasks with no ambiguity. In this work, we demonstrate thatwhile current uncertainty estimators perform well under the restrictiveassumption of no ambiguity, they degrade to close-to-random performance onambiguous data. |
Tim Tomov; Dominik Fuchsgruber; Tom Wollschläger; Stephan Günnemann; | arxiv-cs.LG | 2025-11-06 |
| 990 | Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large language models (LLMs) struggle with this task, frequentlyfailing to interpret user intent (misinterpretation) or unnecessarily alteringthe original question’s structure (over-correction). We propose QuestionRAG, aframework that tackles these problems. |
Longpeng Qiu; Ting Li; Shuai Mao; Nan Yang; Xiaohui Yan; | arxiv-cs.CL | 2025-11-05 |
| 991 | Comparing The Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Evaluationmetrics employed in the study include accuracy and precision for binaryquestions and ranking by a human expert, ranking by Google’s AI model Gemini,alongside cosine similarity for long-answer questions. |
Ranul Dayarathne; Uvini Ranaweera; Upeksha Ganegoda; | arxiv-cs.CL | 2025-11-05 |
| 992 | ChiMDQA: Towards Comprehensive Chinese Document QA with Fine-grained Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: With the rapid advancement of natural language processing (NLP) technologies,the demand for high-quality Chinese document question-answering datasets issteadily growing. To address this issue, we present the Chinese Multi-DocumentQuestion Answering Dataset(ChiMDQA), specifically designed for downstreambusiness scenarios across prevalent domains including academic, education,finance, law, medical treatment, and news. |
Jing Gao; Shutiao Luo; Yumeng Liu; Yuanming Li; Hongji Zeng; | arxiv-cs.CL | 2025-11-05 |
| 993 | Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through attention analysis, we find that learning new knowledgereduces the model’s attention to key entities in the question, thus causingexcessive focus on the surrounding context, which may increase the risk ofhallucination. |
Renfei Dang; Peng Hu; Changjiang Gao; Shujian Huang; | arxiv-cs.CL | 2025-11-04 |
| 994 | A Graph-based RAG for Energy Efficiency Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we investigate the use of Large Language Models (LLMs) within agraph-based Retrieval Augmented Generation (RAG) architecture for EnergyEfficiency (EE) Question Answering. |
RICCARDO CAMPI et. al. | arxiv-cs.CL | 2025-11-03 |
| 995 | Benchmarking Geospatial Question Answering with MapQA Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Geospatial question answering (QA) is a fundamental task in navigation and point of interest (POI) searches, yet existing datasets are limited in scale, diversity, and they rely … |
ZEKUN LI et. al. | Proceedings of the 33rd ACM International Conference on … | 2025-11-03 |
| 996 | DEEPAMBIGQA: Ambiguous Multi-hop Questions for Benchmarking LLM Answer Completeness Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing QA benchmarks rarely evaluateboth challenges jointly. To address this, we introduce DeepAmbigQAGen, anautomatic data generation pipeline that constructs QA tasks grounded in textcorpora and linked knowledge graph, generating natural and verifiable questionsthat systematically embed name ambiguity and multi-step reasoning. |
Jiabao Ji; Min Li; Priyanshu Kumar; Shiyu Chang; Saloni Potdar; | arxiv-cs.CL | 2025-11-03 |
| 997 | Pinpointing Trigger Moment for Grounded Video QA: Enhancing Spatio-temporal Grounding in Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this technical report, we introduce a framework to address Grounded VideoQuestion Answering (GVQA) task for the ICCV 2025 Perception Test Challenge. |
Jinhwan Seo; Yoonki Cho; Junhyug Noh; Sung-eui Yoon; | arxiv-cs.CV | 2025-11-03 |
| 998 | Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a pipeline for automated synthesis for text-VQA dataset thatcan produce faithful QA pairs, and which scales up with the availability ofscene text data. |
Soham Joshi; Shwet Kamal Mishra; Viswanath Gopalakrishnan; | arxiv-cs.CV | 2025-11-03 |
| 999 | TrafficNetQA: Question Answering Datasets for Evaluating LLM Performance on Traffic Network Files Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: We propose TrafficNetQA, a benchmark to quantitatively evaluate how well Large Language Models (LLMs) understand transportation-related traffic networks. While there is increasing … |
Donghoon Kwon; Seungmo Kang; Seongjin Choi; | Proceedings of the 33rd ACM International Conference on … | 2025-11-03 |
| 1000 | When to Trust The Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Question Aligned Semantic Nearest NeighborEntropy (QA-SNNE), a black box uncertainty estimator that incorporates questionsemantics into prediction confidence. |
DENNIS PIERANTOZZI et. al. | arxiv-cs.CV | 2025-11-03 |