arXiv:2606.01041v1 Announce Type: new Abstract: Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods remain confined to explicit text space, retrieving experiences via semantic similarity and concatenating them into the context window, leading to substantial token overhead and a decoupled architecture that separates retrieval from generation. To address these limitations, we propose ExpWeaver, a framework that enables LLM agents to learn from experience via latent retri
The proliferation of LLM agents and their limitations in long-term learning and efficient experience utilization drive the immediate need for frameworks like ExpWeaver.
This development enhances the autonomy and efficiency of LLM agents by addressing key challenges in experience learning, token management, and integration of retrieval with generation.
LLM agents will be able to learn more effectively from past interactions, reducing computational overhead and leading to more sophisticated and capable autonomous systems.
- · AI agent developers
- · Companies deploying autonomous LLM systems
- · Computational researchers in AI
- · Legacy LLM architectures reliant on explicit text retrieval
LLM agents become more robust and less prone to 'forgetting' past interactions.
Reduced operational costs for complex agentic workflows due to more efficient experience recall and utilization.
Acceleration in the development and deployment of fully autonomous AI systems across various industries.
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Read at arXiv cs.CL