AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution (Mar 2026)

Описание: Title: AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution (Mar 2026)
Link: http://arxiv.org/abs/2603.01145v2
Date: March 2026

Summary:
This paper presents AutoSkill, a training-free lifelong learning framework that enables LLM agents to automatically derive, maintain, and reuse skills from interaction traces. Unlike memory-based approaches that treat past dialogues as raw text, AutoSkill abstracts recurring behaviors into explicit, versioned, and human-editable artifacts. This allows agents to accumulate personalized capabilities and stable user preferences across sessions without the need for model retraining.

Key Topics:
Lifelong Learning
Skill Self-Evolution
Large Language Model Agents
Personalization
Experience-Driven Learning
Skill Extraction

Chapters:
00:00 - 60-Second Summary
01:11 - Personalization Friction Challenges
02:27 - RAG vs Operational Behavior
03:51 - Introducing Skill.md Artifacts
05:06 - Dual Loop Architecture
06:03 - Hybrid Skill Retrieval Logic
07:47 - Extracting Human-Driven Skills
09:14 - Managing Skill Evolution
10:58 - Production Deployment Modes
12:29 - Analyzing Real-World Usage
14:04 - Domain-Specific Skill Examples
15:17 - Validating System Scalability
16:35 - Future of Tradable Skills

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