pith:UQQL6U5V
Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization
Metis reformulates jailbreaking as inference-time policy optimization in a POMDP that uses a metacognitive loop to diagnose defenses and steer attacks.
arxiv:2605.10067 v3 · 2026-05-11 · cs.LG · cs.AI
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Claims
Metis achieves the strongest average Attack Success Rate (ASR) among compared methods at 89.2%, maintaining high efficacy on resilient frontier models (e.g., 76.0% on O1 and 78.0% on GPT-5-chat) where traditional baselines exhibit substantial performance degradation.
That the structured feedback extracted from target responses can reliably serve as a semantic gradient capable of steering the policy toward successful jailbreaks without the optimization collapsing into ineffective local patterns on advanced aligned models.
Metis achieves 89.2% average attack success rate across 10 LLMs including 76% on o1 and 78% on GPT-5-chat while cutting token cost by 8.2x on average through metacognitive policy optimization in a POMDP.
Receipt and verification
| First computed | 2026-05-22T01:04:05.886052Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
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Canonical record JSON
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