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DataCOPE is an unsupervised verifier-guided skill discovery framework for data-analytic agents that derives verifier signals from exploration trajectories without labeled supervision. It improves performance by 9.71% and 32.30% on report-style and reasoning-style data analysis tasks respectively.
This paper introduces CARL, a method for offline hierarchical reinforcement learning that exploits local dynamics regularity to learn reusable skills. The approach clusters state-goal pairs requiring similar action sequences, enabling more effective skill reuse and improved performance on complex humanoid tasks.
SkillsVote is an AI Agent Skill management tool that filters 790K+ Skills from GitHub and extracts functional descriptions, environment requirements, and permission information. It supports precise recommendation, execution attribution, and iterative optimization, and can also provide workflow combination suggestions.
This paper proposes the Experience Compression Spectrum, a unifying framework that integrates agent memory, skill discovery, and rule-based systems along a single axis of increasing compression (5-20× for episodic memory, 50-500× for procedural skills, 1000×+ for declarative rules). The work identifies a critical gap—the 'missing diagonal'—showing that existing systems operate at fixed compression levels without adaptive cross-level support, and articulates design principles for scalable, full-spectrum agent learning systems.
SkillFlow introduces a benchmark of 166 tasks across 20 families for evaluating autonomous agents' ability to discover, repair, and maintain skills over time through a lifelong learning protocol. Experiments reveal a substantial capability gap among leading models, with Claude Opus 4.6 improving significantly while others show limited or negative gains from skill evolution.