构建可持续自我进化智能体的框架与评测基准
Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark
- 基于真实交互的终身学习框架，支持持续成长
- 提出四阶段机制，实现经验积累与技能内化
- 提供大学生活全流程评测数据集，适合通用智能研究
随着人工智能向通用智能演进，研究重点正从静态任务优化转向构建能持续学习的开放智能体。本文提出体验驱动的终身学习（Experience-driven Lifelong Learning, ELL）框架，使智能体通过与动态环境的自主交互实现持续成长。该框架包含四个核心原则：(1) 经验探索：智能体在动态环境中主动交互，生成丰富的经验轨迹；(2) 长期记忆：将个人经历、领域知识和常识推理结构化存储于持久记忆系统中；(3) 技能学习：从经验中抽象出可复用的技能，并在新任务中不断验证与优化；(4) 知识内化：将显式经验转化为隐式的本能能力。同时，我们构建了StuLife基准数据集，模拟学生从入学到学术与个人发展的全过程，涵盖三个核心阶段与十项具体场景，为评估智能体的长期学习能力提供统一测试平台。
原文摘要 · Abstract (English)
As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we introduce Experience-driven Lifelong Learning (ELL), a framework for building self-evolving agents capable of continuous growth through real-world interaction. The framework is built on four core principles: (1) Experience Exploration: Agents learn through continuous, self-motivated interaction with dynamic environments, navigating interdependent tasks and generating rich experiential trajectories. (2) Long-term Memory: Agents preserve and structure historical knowledge, including personal experiences, domain expertise, and commonsense reasoning, into a persistent memory system. (3) Skill Learning: Agents autonomously improve by abstracting recurring patterns from experience into reusable skills, which are actively refined and validated for application in new tasks. (4) Knowledge Internalization: Agents internalize explicit and discrete experiences into implicit and intuitive capabilities as "second nature". We also introduce StuLife, a benchmark dataset for ELL that simulates a student's holistic college journey, from enrollment to academic and personal development, across three core phases and ten detailed sub-scenarios. StuLife is designed around three key paradigm
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。