AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
This addresses the need for scalable and reusable personalization in LLM applications, offering a practical solution for lifelong personalized agents, though it appears incremental as it builds on existing memory and retrieval techniques.
The paper tackles the problem of LLM agents failing to accumulate personalized capabilities from repeated user interactions by introducing AutoSkill, a framework that automatically derives, maintains, and reuses skills from dialogue traces, enabling lifelong learning without retraining the underlying model.
In practical LLM applications, users repeatedly express stable preferences and requirements, such as reducing hallucinations, following institutional writing conventions, or avoiding overly technical wording, yet such interaction experience is seldom consolidated into reusable knowledge. Consequently, LLM agents often fail to accumulate personalized capabilities across sessions. We present AutoSkill, an experience-driven lifelong learning framework that enables LLM agents to automatically derive, maintain, and reuse skills from dialogue and interaction traces. AutoSkill abstracts skills from user experience, supports their continual self-evolution, and dynamically injects relevant skills into future requests without retraining the underlying model. Designed as a model-agnostic plugin layer, it is compatible with existing LLMs and introduces a standardized skill representation for sharing and transfer across agents, users, and tasks. In this way, AutoSkill turns ephemeral interaction experience into explicit, reusable, and composable capabilities. This paper describes the motivation, architecture, skill lifecycle, and implementation of AutoSkill, and positions it with respect to prior work on memory, retrieval, personalization, and agentic systems. AutoSkill highlights a practical and scalable path toward lifelong personalized agents and personal digital surrogates.