SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.
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A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
citing papers explorer
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SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System
SkillMAS couples skill evolution and MAS restructuring via utility learning from traces, bounded skill updates, and evidence-gated team changes, reporting competitive results across manipulation, CLI, and retail tasks.
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.