TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews

Abstract:LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well-structured. Existing work either classifies authorship without judging quality, or scores quality with features designed for human-written reviews; no prior system detects deficiencies in LLM-generated reviews at the level of individual defect types. To bridge the gap, we introduce TADDLE, a Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews, together with the first expert-annotated benchmark for this task. Our benchmark comprises 1,800 reviews on 50 ICLR 2025 papers, multi-label-annotated by 18 domain experts against a taxonomy of six defect categories (plus a non-deficient label). TADDLE decomposes detection into four specialized analysis tools -- Verify, Correct, Complete, and Transform -- orchestrated by an agent; an integrator synthesizes their outputs into binary and multi-label classifications via two-stage semi-supervised learning. Experiments show that TADDLE performs strongly on binary detection and the multi-label classification task. We release the benchmark and code at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2605.26911 [cs.AI]

(or arXiv:2605.26911v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.26911 Focus to learn more arXiv-issued DOI via DataCite

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