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Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews

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arXiv:2605. 21713v1 Announce Type: new Abstract: How can we distinguish whether a peer review was written by a human or generated by an AI model?

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Authors: Andr\'e V. Duarte, Brian Tufts, Aditya Oke, Fei Fang, Arlindo L. Oliveira, Lei Li
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, class, line, rate, compare, full, model. Source: arxiv cs.CL (NLP).
arXiv: 2605.21713

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