MSCE 记忆转技能

elvis (@omarsar0) 转发了 DAIR.AI (@dair_ai) 的帖子： Very cool idea to convert memory to skills. (bookmark it) Most agent memory systems retrieve past traces as passive context. MSCE turns them into executable skills instead. The training-free framework organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition. Evidence-backed policies with positive estimated gain get converted into callable skills that carry their applicability boundaries, verification rules, and reliability estimates. So a skill knows when it applies and how to check itself. The other piece is reflection-weighted value backfilling, which propagates sparse terminal feedback through dense local self-reflections to produce evidence-calibrated trace values. Those values govern which memory and skills survive. On EvoAgentBench and LoCoMo, MSCE outperforms strong skill-augmented and memory-driven baselines with cross-domain transfer. The gap between memory-as-context and memory-as-capability seems to be where long-horizon agents actually compound. Paper: https://t.co/rXNkXqsXDx Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c