Very cool idea to convert memory to skills. (bookmark it) Most agent memory systems retrieve past traces as passive context. MSCE turns them…

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://arxiv.org/abs/2607.16621 Learn to build effective AI agents in our academy: https://academy.dair.ai/

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Source: DAIR.AI (X) | 2026-07-21