Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization

Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing approaches often rely on static heuristics or stochastic search, rendering them brittle against advanced safety alignment. To address this, we introduce Metis, a framework that reformulates jailbreaking as inference-time policy optimization within an adversarial Partially Observable Markov Decision Process (POMDP). Metis employs a self-evolving metac

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ethics.ai (11 May 2026), “Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization,” evidence record 4585, https://ethics.ai/record/4585 (originally published by arXiv).

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