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.
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HanXiao
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Isaac
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XiangJinYu
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Yixiong Fang
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Heng Ji
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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)