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

• URL: http://arxiv.org/abs/2605.26911v1
• Date: Tue, 26 May 2026 12:07:44 GMT
• Title: TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
• Abstract summary: We introduce TADDLE, a tool for detecting deficiencies in LLM-generated peer reviews.<n>Our benchmark comprises 1,800 reviews on 50 ICLR 2025 papers, multi-label-annotated by 18 domain experts.<n>Extensive experiments show that TADDLE performs strongly on both binary detection and the multi-label classification task.
• Score: 4.404176366651557
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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. Extensive experiments show that TADDLE performs strongly on both binary detection and the multi-label classification task. We release the benchmark and code at https://github.com/AquariusAQ/TADDLE.

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