AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
arXiv:2603.01145v1 Announce Type: new Abstract: 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 t
arXiv:2603.01145v1 Announce Type: new Abstract: 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.
Executive Summary
The article introduces AutoSkill, a lifelong learning framework that enables Large Language Models (LLMs) to derive, maintain, and reuse skills from user interaction experiences. AutoSkill abstracts skills from user experience, supports their self-evolution, and injects relevant skills into future requests without retraining the model. This framework is designed as a model-agnostic plugin layer, making it compatible with existing LLMs and introducing a standardized skill representation for sharing and transfer across agents, users, and tasks.
Key Points
- ▸ AutoSkill enables LLMs to accumulate personalized capabilities across sessions
- ▸ The framework abstracts skills from user experience and supports their continual self-evolution
- ▸ AutoSkill introduces a standardized skill representation for sharing and transfer across agents, users, and tasks
Merits
Personalization
AutoSkill allows LLMs to learn from user interactions and tailor their responses to individual preferences and requirements
Scalability
The framework is designed as a model-agnostic plugin layer, making it compatible with existing LLMs and enabling seamless integration
Demerits
Complexity
The implementation of AutoSkill may require significant computational resources and expertise, potentially limiting its adoption
Expert Commentary
The introduction of AutoSkill marks a significant step forward in the development of lifelong learning frameworks for LLMs. By enabling LLMs to learn from user interactions and accumulate personalized capabilities, AutoSkill has the potential to revolutionize the field of natural language processing. However, the implementation of AutoSkill also raises important questions about explainability, transparency, and bias, which must be carefully considered in order to ensure the responsible development and deployment of this technology.
Recommendations
- ✓ Further research is needed to fully explore the potential of AutoSkill and address the challenges and limitations associated with its implementation
- ✓ Developers and practitioners should prioritize transparency and explainability in the design and deployment of AutoSkill, in order to build trust and ensure the responsible use of this technology