TBNAI
Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
Publication: cs.AI updates on arXiv.org
Author: Bowen Ye, Yongchao Xu, Junkai Ma, Xiang Yin, Wenzhao Li
Source published: 07/10/2026, 04:00:00 UTC
Briefing created: 04/11/2026, 05:34:58 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:24 UTC.
Briefings
A new framework called SAGA enables LLM agents to evolve by converting interaction experiences into reusable principles rather than relying on parameter updates. Experiments on ScienceWorld and ALFWorld show improved task performance, with ablation studies confirming that contextual instantiation and action regulation are essential for effective knowledge transfer.
Key points
- The SAGA framework transforms interaction trajectories into episodic descriptions, reusable procedures, and principles with explicit applicability conditions.
- Retrieved principles are instantiated into task-specific guidance and used to refine candidate actions through corrective feedback and resampling.
Context
This research addresses the limitation where existing methods focus on experience representation but fail to generalize acquired knowledge beyond specific contexts.
Limitations
The study is limited to ScienceWorld and ALFWorld environments; ablation studies confirm the importance of contextual instantiation and action regulation, but the generalizability of these findings across diverse domains remains unverified.
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