AI2 and UW Re-Evaluate Harness Evolution: Self-Evolving Agents or Just More Attempts?
jiqizhixin · x · 2026-09-24
AI2 and the University of Washington re-evaluate Harness Evolution, where agents analyze their own failures and modify prompts, tools, memory, control logic—even rewriting the runtime framework.
- The hidden question: when a self-evolving agent scores higher, is it genuinely learning better strategies, or simply getting more inference attempts than a plain agent?
- The researchers compare Harness Evolution against the simplest "rerun several times, pick the best" baseline under as fair conditions as possible.
- The work is a reminder that reported agent gains may partly come from extra compute budget rather than the method itself.
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