The paper introduces CLAIMPROBE, a claim-level audit tool that breaks down deep-research reports into individual claims and evaluates them for hallucination, misattribution, citation hygiene, and necessary-fact recall against retrieved evidence. Using CLAIMPROBE, the authors show that even high-scoring deep-research pipelines can omit key evidence and misattribute claims. They also propose CLAIMWRITER, a hierarchical claim-based writer that extracts source facts, maps them to an outline, and drafts sections from a source-linked claim representation, which reduces hallucination by 2.6 to 4.5 times and improves necessary-fact recall by 1.2 to 1.7 times while preserving overall report quality and enabling efficient localized revisions.
arXiv:2604. 04074v4 Announce Type: replace Abstract: Large language model (LLM)-based reviewing systems typically assess manuscripts in isolation, leaving literature- and code-dependent claims difficult to verify.
The paper introduces a claim‑gated audit framework for generative search, ensuring that a query, source, and answer tuple is only considered resolved when relationship evidence, answer adoption, materiality, and disclosure are all present. It distinguishes this audit endpoint from citation support and review priority, tying decisions to versioned evidence spans and implementing a reference checker to enforce the contract. Experiments on a synthetic dataset confirm that the system correctly handles all 81 predicate combinations and rejects 192 malformed records, while ablation studies isolate endpoint logic from missing‑evidence handling.
arXiv:2606. 23989v1 Announce Type: cross Abstract: End-to-end large language models (LLMs) produce fluent multi-document summaries but remain prone to hallucination, and the attributions they offer are typically coarse (whole documents or passages) and generated post hoc, leaving each summary statement hard to verify.
arXiv:2609.25046v1 Announce Type: new Abstract: Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely...
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:2602. 18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action.
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
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:2607. 17291v1 Announce Type: new Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents.
arXiv:2609.10293v1 Announce Type: new Abstract: In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the sou...
The paper introduces CAMS, a Claim‑Anchored Multi‑Document Summarization framework that decomposes source documents into atomic claims, resolves provenance deterministically from verbatim quotes to token spans, clusters equivalent claims across documents, and rewrites summaries so each sentence ends with claim identifiers linking back to source spans. CAMS separates provenance (an invariant for each emitted sentence) from faithfulness (an objective encouraged by selection, rewriting, and verification). Evaluations on MultiNews, DiverseSumm, and zero‑shot WCEP show that CAMS matches strong baselines in summary quality while improving faithfulness and citation precision, raising attribution accuracy from 38% to 64% and reducing human verification time per claim by 3.4×.
arXiv:2608.25336v2 Announce Type: replace Abstract: Large language models (LLMs) can fluently verbalize statistical evidence, yet statistical reports can still drift numerical values, invert effect d...