arXiv: cs.AI preprintsInternational6 November 2026
SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents
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arXiv:2609.35596v3 Announce Type: replace-cross Abstract: Self-evolving LLM agents have gained prominence for their ability to improve after deployment by modifying their harness, including their controller instructions, memory management protocols, and reusable tools and skills, in response to user and environment feedback. However, locally useful updates may persist into later tasks where they produce unsafe behavior, even without direct adversarial influence. To study this risk, we introduce SEABench, a benchmark for studying endogenous misalignment arising from agent self-evolution, with 4
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