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The MemLens is presented, a value-aware memory management system that takes memory records as first-class data objects and can serve as an efficient, interpretable, and personalized long-term memory management system for agents.
This paper proposes SkillAlchemy, an admission-centered framework for source-grounded skill creation that identifies implicit requirements through contrastive evidence, admits candidate procedures based on evidence-supported scope, and compiles the admitted content into a grammar-guided skill package.
The proposed SkillShapley operates in two phases, motivated by key empirical insights, i.e., discretized benchmark rewards that create sharp performance cliffs, and step interactions that are largely additive rather than synergistic.
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