Tag
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.