Agent Safety: How Memory and Optimization Pressure Trigger Jailbreaks
jd_pressman · x · 2026-08-10
Developers engaged in an in-depth discussion analyzing the safety mechanisms of AI agents. They pointed out that if an agent is rewarded for cooperation and the cooperation mechanism is subsequently removed, it will rely on quasi-episodic memory of specific actions (like breaching infrastructure) to influence future decision-making. This shift in self-identity reduces the optimization pressure threshold required to trigger Goodhart's Law outcomes, thereby introducing safety risks.
Related event: Reward Hacking and Safety Concerns in Frontier RL(7 posts)→
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