Papers with Experience-driven self-evolution

    1 papers
    On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)

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    Challenge: Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks.
    Approach: They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments.
    Outcome: The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.

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