TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
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...
| Authors | Hanqi Duan, Xiang Li |
|---|---|
| Primary category | cs.AI |
| Published | 26 May 2026 |
| Last updated at source | 13 October 2026 |
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