MSCE Framework: Turning Agent Memory into Executable Skills
dair_ai · x · 2026-07-22
Highlights MSCE, a training-free framework that converts agent memory into executable skills.
Framework Mechanics:
- Memory to Skills: Unlike traditional systems that use past traces as passive context, MSCE organizes agent experiences into step traces and procedural policies. Policies with positive estimated gains are converted into callable skills, complete with their applicability boundaries, verification rules, and reliability estimates.
- Reflection-Weighted Value Backfilling: It propagates sparse terminal feedback through dense local self-reflections to produce evidence-calibrated trace values, which govern which memories and skills survive.
On the EvoAgentBench and LoCoMo benchmarks, MSCE outperforms strong skill-augmented and memory-driven baselines while demonstrating cross-domain transfer capabilities.
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