Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction

Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experience remains limited. Although fine-tuning provides a pathway for adaptation, its reliance on parameter access and substantial computational resources limits its flexibility and scope of application, particularly for large-scale and closed-source LLMs. External memory provides an alternative by enabling agents to accumulate experience without modifying model parameters. However, existing approaches primarily focus on how experiences are represented and organized, while the acquired knowledge often remains closely coupled with specific tasks and contexts, limiting its generalization for continual agent evolution. A fundamental challenge is therefore how to transform concrete interactions into abstract and reusable knowledge that can guide future decisions and enable agents to generalize beyond individual experiences. To address this issue, we propose SAGA (Self-evolving Agents through Experience-Grounded Abstraction), a framework for experience-grounded knowledge abstraction and utilization in LLM agents. SAGA progressively transforms interaction trajectories into episodic descriptions, reusable procedures, and principles with explicit applicability conditions, while preserving links to their supporting execution evidence. To make abstract knowledge actionable, SAGA instantiates retrieved principles into task-specific operational guidance and uses principle-derived checks to refine candidate actions through corrective feedback and resampling. These mechanisms form an execution–abstraction feedback loop, in which accumulated knowledge guides subsequent interactions and newly collected experiences update the hierarchical memory. Experiments on ScienceWorld and ALFWorld demonstrate improved task performance, while ablation studies show that contextual instantiation and action regulation are important for realizing the benefits of principle-level knowledge.