How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review
Uses 4,200 manuscripts derived from 120 ICLR 2026 submissions to test how six rhetorical dimensions (evidence framing, novelty stance, scope framing, etc.) shift scores from five LLM reviewers under standard and strict protocols. Evidence framing and novelty stance drive the largest score contrasts; lower scores tend to rise, higher scores tend to fall, and strict review lowers mean overall assessment by 1.36 points without reducing rhetorical sensitivity.
Why it matters
A controlled empirical answer to a question that affects every LLM-as-judge pipeline: which rhetorical moves most reliably move the score, and how reliably reviewers can be re-stabilized by protocol changes.
Importance: 2/5
default base 2 (no notable bumps)
Sources
official
arXiv 2608.08975