ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning 事件

ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning arXiv:2605.16309v2 Announce Type: replace Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, and constraints--remains unrepaired. Existing self-evolving approaches address this gap by updating prompts, memory, or model weights, but none directly repair the symbolic structures that encode how task