{"ID":2878104,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19005","arxiv_id":"2508.19005","title":"Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark","abstract":"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","short_abstract":"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-...","url_abs":"https://arxiv.org/abs/2508.19005","url_pdf":"https://arxiv.org/pdf/2508.19005v6","authors":"[\"Yuxuan Cai\",\"Yipeng Hao\",\"Jie Zhou\",\"Hang Yan\",\"Zhikai Lei\",\"Rui Zhen\",\"Zhenhua Han\",\"Yutao Yang\",\"Junsong Li\",\"Qianjun Pan\",\"Tianyu Huai\",\"Qin Chen\",\"Xin Li\",\"Kai Chen\",\"Bo Zhang\",\"Xipeng Qiu\",\"Liang He\"]","published":"2025-08-26T13:04:28Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\"]","methods":"[\"Large Language Model\",\"LoRA\"]","has_code":false}