Measuring LLM-Associated Signatures in Peer Review: A Comparative Study of ICLR 2025 and 2026

Large Language Models (LLMs) are increasingly used to support academic peer review through linguistic refinement and content generation. However, detectors based primarily on surface-level writing patterns may conflate language editing with automated scientific judgment. This paper presents a comparative empirical study of 300 ICLR 2025–2026 submissions and 1,201 linked official reviews, including 75 accepted and 75 rejected papers from each year. SemDetect, a claim-level semantic detection framework, is used to distinguish human-associated, rewrite-associated, and AI-associated signatures. Of 1,178 scorable reviews, 776 (65.9%) are classified as rewrite associated, 401 (34.0%) as human associated, and one (0.1%) as AI associated; 23 reviews are retained separately as unscorable. Human-associated and rewrite-associated reviews have similar mean ratings (5.11 versus 5.06) and reviewer-reported confidence (3.58 versus 3.57), but rewrite-associated reviews exhibit lower raw rating dispersion (1.69 versus 1.89). At the paper level, rewrite proportion is negatively correlated with within-paper rating variance, although the association does not meet the conventional significance threshold after adjustment for mean rating, review count, and conference cohort ($p=0.067$). Rewrite prevalence is also similar for accepted and rejected papers (66.3% versus 65.5%) and is not independently associated with final decisions. The findings indicate that the dominant detected signature is refinement rather than complete review generation. They also show that rating dispersion provides complementary information to average ratings, although the adjusted evidence for variance compression remains inconclusive. Because detection cannot establish authorship, all outputs are interpreted as probabilistic indicators rather than proof of individual LLM use.