arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse successful strategies or failure lessons from prior experience. Fine-tuning on collected experience can improve reuse, but it is inflexible when stronger or more suitable executors emerge. We propose ExpGraph, a model-agnostic experience learning framework that enables frozen and replaceable LLM executors to improve through external experience reuse without parameter updates
The rapid advancement and widespread deployment of LLMs highlight the immediate need for agents that can learn and adapt rather than redundantly solve problems from scratch.
This development addresses a critical limitation of current LLM agents by enabling continuous improvement and adaptability without costly retraining or architectural changes, which is crucial for their scaling and usability.
LLM agents can now leverage past experiences more effectively, allowing for greater efficiency and robustness in task execution, and potentially accelerating their integration into complex workflows.
- · AI software developers
- · Enterprises adopting LLM agents
- · Cloud computing providers
- · Companies relying on static, non-learning LLM deployments
- · Workflow automation solutions without agentic capabilities
LLM agents become more capable and autonomous, reducing human oversight in complex tasks.
The cost-effectiveness of deploying LLM agents increases significantly as they learn and optimize their own processes.
This could lead to a ' Cambrian explosion' of novel LLM agent applications across industries as their reliability and adaptability improve.
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Read at arXiv cs.CL