SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment
SafeEvolve co-evolves agent harnesses and safety policies from runtime experience, addressing the gap where static guardrails fail to keep pace with multi-step LLM agent behaviours.
Summary written by editorial AI · Source link below
arXiv:2609.02786v1 Announce Type: cross Abstract: The performance of LLM-based agents is jointly shaped by the base model and the harness used when interacting with the environment. This exposes them to safety risks in both harmful final responses and multi-step execution trajectories. Existing safety alignment mechanisms often rely on either external harness updates or policy optimization, yet applying either paradigm in isolation fails to bridge runtime control with intrinsic safety. We propo
Editorial Analysis
Static safety guardrails degrade as agent behaviours evolve; dynamic co-evolution could improve alignment durability for enterprise AI deployments.
Evaluate dynamic safety-alignment approaches like harness-policy co-evolution when deploying multi-step LLM agents.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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