SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents
Quick summary
arXiv:2609.35596v2 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
Key takeaways
- arXiv:2609.35596v2 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
Why it matters
“SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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