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NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment [54.4265627247104]
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arXiv Detail & Related papers (2026-09-10T08:34:56Z)
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Deep research (DR) systems produce long-form cited reports by orchestrating multiple agents that search and synthesize information from the web.<n> Citations are the primary mechanism for evaluating the faithfulness of these reports, yet current DR systems exhibit poor citation recall.<n>We propose an evaluation method that pinpoints which agent introduced each error by locally testing agent invocations for faithfulness and verifiability relative to their own inputs.
arXiv Detail & Related papers (2026-08-25T09:34:18Z)
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We developed Plato-Bio, a biology-routed extension of the open Plato/Denario architecture.<n>Plato-Bio couples explicit workflow states with provenance records, citation checks, claim-to-evidence links, scoped file writes, and publication gates.<n>On the current clean revision, the full Python suite completed with 931 passes, six skips, and no failures or errors.
arXiv Detail & Related papers (2026-07-27T03:55:39Z)
Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences [4.766369673059568]
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ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence [57.37494162084001]
Chain-of-Evidence (CoE) is a verifiability framework requiring every claim to be traceable to its evidence source.<n>CoE Audit is a post-hoc audit whose four integrity checks -- score verification, specification violation, reference verification, and method-code alignment -- apply uniformly to all systems.
arXiv Detail & Related papers (2026-05-25T21:30:27Z)
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arXiv Detail & Related papers (2026-05-08T13:26:41Z)
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arXiv Detail & Related papers (2026-01-15T06:25:25Z)
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arXiv Detail & Related papers (2025-12-17T04:22:28Z)
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We report on the first preregistered empirical evaluation of AI-driven legal research tools. We find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. It provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs.
arXiv Detail & Related papers (2024-05-30T17:56:05Z)
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Large language models (LMs) are prone to generate factual errors, which are often called hallucinations. We introduce a comprehensive taxonomy of hallucinations and argue that hallucinations manifest in diverse forms. We propose a novel task of automatic fine-grained hallucination detection and construct a new evaluation benchmark, FavaBench.
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AI Hallucinations: A Misnomer Worth Clarifying [4.880243880711163]
We present and analyze definitions obtained across all databases, categorize them based on their applications, and extract key points within each category. Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature.
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A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation [76.34411067299331]
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arXiv Detail & Related papers (2023-07-08T14:25:57Z)
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We analyze the connection between hallucinations and training data, and find evidence that models hallucinate because they train on target summaries that are unsupported by the source. We present PINOCCHIO, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations.
arXiv Detail & Related papers (2022-03-16T07:13:52Z)
Deep Graph Learning for Anomalous Citation Detection [55.81334139806342]
We propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks. Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts.
arXiv Detail & Related papers (2022-02-23T09:05:28Z)

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