A Survey of Self-Evolving Agents: On Path to Artificial Super
  Intelligence
Large Language Models (LLMs) have demonstrated strong capabilities but remain
fundamentally static, unable to adapt their internal parameters to novel tasks,
evolving knowledge domains, or dynamic interaction contexts. As LLMs are
increasingly deployed in open-ended, interactive environments, this static
nature has become a critical bottleneck, necessitating agents that can
adaptively reason, act, and evolve in real time. This paradigm shift -- from
scaling static models to developing self-evolving agents -- has sparked growing
interest in architectures and methods enabling continual learning and
adaptation from data, interactions, and experiences. This survey provides the
first systematic and comprehensive review of self-evolving agents, organized
around three foundational dimensions -- what to evolve, when to evolve, and how
to evolve. We examine evolutionary mechanisms across agent components (e.g.,
models, memory, tools, architecture), categorize adaptation methods by stages
(e.g., intra-test-time, inter-test-time), and analyze the algorithmic and
architectural designs that guide evolutionary adaptation (e.g., scalar rewards,
textual feedback, single-agent and multi-agent systems). Additionally, we
analyze evaluation metrics and benchmarks tailored for self-evolving agents,
highlight applications in domains such as coding, education, and healthcare,
and identify critical challenges and research directions in safety,
scalability, and co-evolutionary dynamics. By providing a structured framework
for understanding and designing self-evolving agents, this survey establishes a
roadmap for advancing adaptive agentic systems in both research and real-world
deployments, ultimately shedding lights to pave the way for the realization of
Artificial Super Intelligence (ASI), where agents evolve autonomously,
performing at or beyond human-level intelligence across a wide array of tasks.
68886bb5af872d625c10c6ba
Huan-ang Gao
claimed_verified
2025-08-26T09:51:00.700Z
64b1303bf460afaefcf922c2
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/0zhdXXravx0CKJLTSVDwU.jpeg
Huan-ang Gao
user
c7w
68886bb5af872d625c10c6bb
Jiayi Geng
68886bb5af872d625c10c6bc
Wenyue Hua
claimed_verified
2025-08-06T19:32:56.971Z
639a25aba2b0b1c9d85a51e8
https://cdn-avatars.huggingface.co/v1/production/uploads/639a25aba2b0b1c9d85a51e8/pphz-MK62hPNbBkMHAkeR.jpeg
Wenyue Hua
user
wenyueH
68886bb5af872d625c10c6bd
Mengkang Hu
68886bb5af872d625c10c6be
Xinzhe Juan
68886bb5af872d625c10c6bf
Hongzhang Liu
claimed_verified
2025-07-29T07:07:22.183Z
6632160088f75d987d1a156f
https://cdn-avatars.huggingface.co/v1/production/uploads/6632160088f75d987d1a156f/mYlMQfK1BGWeEbOSMmeSb.jpeg
Hongzhang Liu
user
Alphamasterliu
68886bb5af872d625c10c6c0
Shilong Liu
68886bb5af872d625c10c6c1
Jiahao Qiu
68886bb5af872d625c10c6c2
Xuan Qi
68886bb5af872d625c10c6c3
Yiran Wu
68886bb5af872d625c10c6c4
Hongru Wang
68886bb5af872d625c10c6c5
Han Xiao
claimed_verified
2025-07-29T12:50:59.442Z
666aa99cd1652853e4f9a8b9
/avatars/7cd5a0c34b5ccb8eff5a353d88d15a93.svg
HanXiao
user
HanXiao1999
68886bb5af872d625c10c6c6
Yuhang Zhou
68886bb5af872d625c10c6c7
Shaokun Zhang
68886bb5af872d625c10c6c8
Jiayi Zhang
claimed_verified
2025-07-29T07:07:24.616Z
65f40e83653c231cbaf7defe
/avatars/afa5ce72324112739e539865c9aee26b.svg
Jiayi Zhang
user
didiforhugface
68886bb5af872d625c10c6c9
Jinyu Xiang
claimed_verified
2025-07-29T07:07:19.166Z
649ea7106282cb41e77760bc
https://cdn-avatars.huggingface.co/v1/production/uploads/649ea7106282cb41e77760bc/HlWjaqxr03ob93vdKg_LQ.jpeg
Isaac
user
XiangJinYu
68886bb5af872d625c10c6ca
Yixiong Fang
68886bb5af872d625c10c6cb
Qiwen Zhao
68886bb5af872d625c10c6cc
Dongrui Liu
68886bb5af872d625c10c6cd
Qihan Ren
claimed_verified
2025-07-29T14:37:45.288Z
66e2624a436a1798365e4581
/avatars/6c605807d34faa8fb505e135a4b47776.svg
Qihan Ren
user
jasonrqh
68886bb5af872d625c10c6ce
Cheng Qian
68886bb5af872d625c10c6cf
Zhenghailong Wang
68886bb5af872d625c10c6d0
Minda Hu
68886bb5af872d625c10c6d1
Huazheng Wang
claimed_verified
2025-07-30T09:05:13.677Z
688969c4d0ac71994caa0df6
/avatars/f8f89e0ca810908cfe21c921d8373e28.svg
Huazheng Wang
user
huazhengwang
68886bb5af872d625c10c6d2
Qingyun Wu
claimed_verified
2025-07-30T09:05:11.919Z
647fa485d0cd8be13e662973
https://cdn-avatars.huggingface.co/v1/production/uploads/647fa485d0cd8be13e662973/_QLgqLWhWir8_bkDCdTx6.png
Qingyun Wu
user
qingyun-wu
68886bb5af872d625c10c6d3
Heng Ji
68886bb5af872d625c10c6d4
Mengdi Wang
A Survey of Self-Evolving Agents: On Path to Artificial Super
  Intelligence
Large Language Models (LLMs) have demonstrated strong capabilities but remain
fundamentally static, unable to adapt their internal parameters to novel tasks,
evolving knowledge domains, or dynamic interaction contexts. As LLMs are
increasingly deployed in open-ended, interactive environments, this static
nature has become a critical bottleneck, necessitating agents that can
adaptively reason, act, and evolve in real time. This paradigm shift -- from
scaling static models to developing self-evolving agents -- has sparked growing
interest in architectures and methods enabling continual learning and
adaptation from data, interactions, and experiences. This survey provides the
first systematic and comprehensive review of self-evolving agents, organized
around three foundational dimensions -- what to evolve, when to evolve, and how
to evolve. We examine evolutionary mechanisms across agent components (e.g.,
models, memory, tools, architecture), categorize adaptation methods by stages
(e.g., intra-test-time, inter-test-time), and analyze the algorithmic and
architectural designs that guide evolutionary adaptation (e.g., scalar rewards,
textual feedback, single-agent and multi-agent systems). Additionally, we
analyze evaluation metrics and benchmarks tailored for self-evolving agents,
highlight applications in domains such as coding, education, and healthcare,
and identify critical challenges and research directions in safety,
scalability, and co-evolutionary dynamics. By providing a structured framework
for understanding and designing self-evolving agents, this survey establishes a
roadmap for advancing adaptive agentic systems in both research and real-world
deployments, ultimately shedding lights to pave the way for the realization of
Artificial Super Intelligence (ASI), where agents evolve autonomously,
performing at or beyond human-level intelligence across a wide array of tasks.
This survey reviews self-evolving agents, focusing on mechanisms for adaptation in components like models, memory, and architecture, and discusses challenges in safety, scalability, and co-evolutionary dynamics.
self-evolving agents continual learning adaptation intra-test-time inter-test-time scalar rewards textual feedback single-agent systems multi-agent systems evaluation metrics benchmarks coding education healthcare Artificial Super Intelligence (ASI)