SAGA: experience-grounded knowledge abstraction for self-evolving LLM agents (arXiv:2610.06964v1)
This paper introduces SAGA, a framework that progressively abstracts LLM agent interaction trajectories into hierarchical memory entries (episodic descriptions, reusable procedures, and principles with applicability conditions) linked to execution evidence. Retrieved principles are instantiated as task-specific guidance to refine actions, creating a feedback loop that improves performance in interactive benchmarks (ScienceWorld, ALFWorld) and where ablations show the importance of contextual instantiation and action regulation.