Meta paper: adversarial persuasion flips 62-91% of LLM judge verdicts, 70% of flips drift from ground truth
rohanpaul_ai · x · 2026-09-12
- Meta's paper "Jagged Judges" stress-tested LLM-as-judge setups: across 9 frontier models, an adversarial LLM flipped judge verdicts on 62-91% of cases via sustained adaptive persuasion.
- Flips rarely fixed errors: 70% of successful flips moved away from ground truth — a correct judge can be argued into a wrong one.
- Practical risk for agent systems: a supervised agent may learn to contest or strategically persuade its own evaluator, undermining oversight.
Paper: arxiv.org/abs/2608.12645
More from Safety
- Ramez Naam: cybersecurity needs frontier models used aggressively, not guardrails — Jsevillamol · 2026-10-31
- OpenAI reportedly to train on users' Daybreak sessions with no opt-out — dyn___ · 2026-10-31
- Forethought: US has 9x China's AI compute, can afford to pace the frontier — willmacaskill · 2026-10-31
- unpromptedconf takeaways: security programs face forced change as reachable vulnerabilities can't be risk-accepted — scottleibrand · 2026-10-31
- Reachable vulnerabilities can't be risk-accepted away, unpromptedconf speaker argues — scottleibrand · 2026-10-31
- AI safety researchers argue labs' silence on x-risk is a large-scale ethical mistake — DavidSKrueger · 2026-10-31