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.
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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...
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: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...
arXiv:2607. 18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent.
arXiv:2610.01023v1 Announce Type: cross Abstract: Coding agents can return plausible patches that omit required behavior. These failures are hard to review because long traces and confident summaries...
The paper introduces an author-facing large language model (LLM) system that generates a broad set of atomic concerns about a manuscript and compresses them into a concise report, aiming to provide early peer‑review feedback. Evaluations on 3,398 ICLR 2026 submissions show that the system covers 44.9% of historical reviewer issues on a diagnostic set, rising to 78.7% strict coverage and 84.9% seriousness‑weighted coverage after deduplication and refill, using 3.6× more requests and 5.2× more tokens. Ablation studies reveal that representative selection and matcher sensitivity are key factors limiting the compression quality.