Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
Quick Answer
The SAGA framework enables large language model agents to evolve by transforming interactions into reusable knowledge, enhancing task performance in environments like ScienceWorld and ALFWorld.
Quick Take
This approach overcomes the limitations of traditional fine-tuning by utilizing external memory for knowledge abstraction, leading to improved decision-making and adaptability without altering model parameters.
Key Points
- SAGA transforms interaction trajectories into episodic descriptions and reusable procedures.
- The framework maintains links to execution evidence for better decision-making.
- Experiments show improved task performance in ScienceWorld and ALFWorld.
- Ablation studies highlight the importance of contextual instantiation.
- Knowledge abstraction creates a feedback loop for continuous learning.
DeepSignal Analysis
What happened
The SAGA framework introduces a method for large language model agents to evolve by transforming interactions into reusable knowledge. This approach enhances task performance in environments like ScienceWorld and ALFWorld without the need for traditional fine-tuning, which can be computationally expensive and inflexible.
Key evidence
- SAGA allows agents to accumulate experience using external memory, avoiding modifications to model parameters, which is a limitation of traditional fine-tuning.
- The framework transforms interaction trajectories into episodic descriptions and reusable procedures, linking them to execution evidence for better decision-making.
- Experiments conducted on ScienceWorld and ALFWorld show that SAGA improves task performance, highlighting the significance of contextual instantiation and action regulation.
Why it matters
This framework addresses the limitations of existing methods that focus on experience representation but fail to generalize knowledge across different tasks. By enabling agents to abstract knowledge from interactions, SAGA enhances adaptability and decision-making capabilities, which is crucial for the development of more intelligent AI systems.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 enables adaptation, its dependence on parameter access and high computational costs restrict its flexibility, especially for large-scale and closed-source LLMs. External memory offers an alternative by allowing agents to accumulate experience without modifying model parameters. However, existing methods mainly focus on experience representation and organization, while the acquired knowledge remains tightly coupled with specific tasks and contexts, limiting generalization. A key challenge is how to transform concrete interactions into abstract and reusable knowledge that guides future decisions beyond individual experiences.
To address this challenge, we propose SAGA (\underline{\textbf{S}}elf-evolving \underline{\textbf{A}}gents through Experience-\underline{\textbf{G}}rounded \underline{\textbf{A}}bstraction), 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 maintaining links to execution evidence. Retrieved principles are instantiated into task-specific guidance and used to refine candidate actions through corrective feedback and resampling. This creates an execution--abstraction feedback loop, where accumulated knowledge guides future interactions and new experiences continuously update hierarchical memory. Experiments on ScienceWorld and ALFWorld demonstrate improved task performance, with ablation studies highlighting the importance of contextual instantiation and action regulation for leveraging principle-level knowledge.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.06964 [cs.AI] |
| (or arXiv:2610.06964v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06964 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bowen Ye [view email]
[v1]
Sat, 3 Oct 2026 17:29:38 UTC (1,404 KB)
— Originally published at arxiv.org
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