DuQUAD is a dual‑view framework for multi‑document question answering that addresses the "Lost in the Middle" problem by combining a Local Agent guided by a Structural Fusion Score with a Global Agent that reasons over the entire document set. It further prunes evidence using explicit quality scoring and sentence‑level refinement to reduce noise. The approach outperforms existing mitigation baselines, improving accuracy by an average of 3.0% and gold evidence recall by 9.4%, while cutting computational cost.
arXiv:2501. 06286v2 Announce Type: replace-cross Abstract: Multi-hop question answering requires a system to identify and integrate evidence distributed across documents, yet large language models remain vulnerable to irrelevant context.
PRISM is an agentic retrieval framework that uses large language models in a structured loop to improve evidence gathering for multi‑hop question answering. It splits retrieval into three specialized agents—a Question Analyzer, a Selector focused on precision, and an Adder focused on recall—whose iterative interaction yields a compact yet comprehensive evidence set. Experiments on HotpotQA, 2WikiMultiHopQA, MuSiQue, and MultiHopRAG show that PRISM consistently outperforms strong baselines by achieving higher retrieval accuracy and filtering out distracting content.
DeepWeaver is a framework designed to improve open‑ended question answering by weaving noisy retrieved evidence into comprehensive, well‑cited answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and supporting evidence, and uses subordinate TBCs to refine and expand the evidence before final generation. Evaluations on LoQA and DeepResearch Bench show that DeepWeaver enhances content sufficiency, citation grounding, and detail preservation across multiple LLMs.
arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunk...
arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.
DocSkill is an agentic framework that learns to orchestrate skills for adaptive evidence acquisition and reasoning in multilingual multi-hop document question answering. It uses bounded Monte Carlo Tree Search to generate skill trajectories for supervised fine‑tuning and preference optimization, and introduces the MMHop-DocBench dataset with 60K QA pairs across 10 languages and eight domains. Experiments show that DocSkill with Qwen3‑VL‑8B outperforms baselines on MMHop‑DocBench and performs competitively on related benchmarks.
arXiv:2609.37491v1 Announce Type: cross Abstract: Retrieval-augmented language models are expected to answer from the retrieved evidence, but in practice they often keep answering when that evidence...
arXiv:2605.28093v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering...
DeepWeaver addresses the evidence synthesis gap in open‑ended question answering by weaving noisy retrieved evidence into comprehensive answers. It introduces Thought Block Chains (TBCs) that organize claims, key information, and citations, allowing the system to revise and expand evidence before final generation. Evaluations on LoQA and DeepResearch Bench show improved content sufficiency, citation grounding, and detail preservation across multiple LLMs.
arXiv:2610.09412v1 Announce Type: cross Abstract: Retrieval diversification is widely available in retrieval-augmented generation (RAG) frameworks, yet prior studies disagree on whether it improves r...
The paper introduces RAG‑Stress, a diagnostic protocol that tests how retrieval‑augmented generation models handle misleading evidence. By fixing the question and reference answer, editing a single assertion to support an incorrect answer, and varying source‑priority policies and evidence positions, the study measures misleading rate (MR) and clean accuracy across fifteen systems on multiple QA datasets. Results show that prioritizing documents increases MR by 10.9–13.5 percentage points, with the highest MR when the misleading span appears at the end of the evidence.
VELD-QA is a large-scale benchmark for visual evidence grounding and question answering in long documents, comprising 15,000 human-authored question-answer pairs across 5,859 PDFs with 372,445 pages. The dataset includes detailed annotations of supporting pages, visual evidence elements, and descriptions of cited visual content, enabling separate evaluation of retrieval, localization, and interpretation. Experiments on nine vision-language models and various retrieval-augmented systems reveal significant gaps in evidence localization and visual interpretation, with oracle accuracy peaking at 81.7% and enrichment of visual-element annotations narrowing performance gaps by 17–24%.