Papers Answer Generation
“Answer Generation” 태그가 달린 논문 485편 · 필터 해제
ViewMind3D: Modular View-Aware Inference for Training-Free 3D-QA
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled new possibilities for 3D question answering (3D-QA), a key capability for embodied AI and robotic perception. However, most e…
Question AnsweringSpatial ReasoningAnswer Generation3D ReconstructionPenelope: Localized Latent Recurrence for Efficient Structured Reasoning
Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens. T…
Answer GenerationCAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering
Long-form question answering increasingly relies on retrieved evidence to make LLM outputs verifiable, with inline citations tracing claims to source documents. However, existing systems often attach citations that are t…
Question AnsweringAnswer GenerationLAMAR: An Open Language-Aware Multilingual Alignment Reranker
In multilingual retrieval augmented generation, a retriever can retrieve relevant documents written in multiple languages, which are subsequently reranked before answer generation. However, it remains unclear whether exi…
Answer GenerationAttention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs
Understanding how vision-language models (VLMs) interpret data visualizations remains an open problem, and is increasingly important as these models are used for analytical tasks where reliable reasoning is essential. We…
Answer GenerationText GenerationDS@GT ARC at LongEval: Citation Integrity and Factual Grounding in Scientific QA
This paper describes DS@GT ARC's submission to the CLEF 2026 LongEval Task 4 on Retrieval-Augmented Generation (RAG). In this submission, we examine a divergence between traditional natural language evaluation metrics an…
Answer GenerationQUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers
Retrieval-augmented question answering depends on selecting evidence passages that jointly support answer generation. However, many RAG pipelines rely on top-\(k\) ranking, where passages are selected mainly by individua…
Question AnsweringAnswer GenerationFuture Confidence Distillation in Large Language Models
Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation depend on accurately es…
Answer GenerationThink Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?
Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing sys…
Answer GenerationMMAgent-R$^2$: Learning to Rerank and Reject for Agentic mRAG
Knowledge-based Visual Question Answering (KB-VQA) requires models to retrieve visual entities matching the query image from large-scale encyclopedic knowledge bases and answer related questions. Existing multimodal Retr…
Visual Question AnsweringAnswer GenerationRSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where int…
Multi-hop Question AnsweringAnswer GenerationKnowledge GraphsReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often…
Answer GenerationTRACE: State-Aware Query Processing over Temporal Evidence Graphs for Conversational Data
Conversational data is increasingly used as a persistent source of user state for long-running assistants and AI agents. However, querying this data remains challenging because conversations naturally evolve: plans are r…
Answer GenerationCoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation
Uncertainty estimation has been a long-standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf. the Dunning-Kruger effect). Although it…
Visual Question AnsweringAnswer GenerationARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on te…
Semantic RetrievalQuestion AnsweringAnswer GenerationMedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes
Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems. Within an outpatient episode, they acquire evidence, use examination and consultation resources, and decide when to…
Answer GenerationProvenAI: Provenance-Native Traces of Evidence in Generated Answers
Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output. This paper introduces ProvenAI, a framewo…
Multi-hop Question AnsweringAnswer GenerationEG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence
Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predominantly evaluated through answer correctne…
Video Question AnsweringAnswer GenerationVinQA: Visual Elements Interleaved Long-form Answer Generation for Real-World Multimodal Document QA
Real-world documents combine text with tables, charts, photographs, and diagrams arranged in diverse layouts, yet existing research on multimodal large language models (MLLMs) for document QA predominantly produces text-…
Answer GenerationWhat Should a Streaming Video Model Remember?
Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets. Existing methods address this by …
Answer Generation