Paper Questions the Evolutionary Gains of Agent Harnesses
dair_ai · x · 2026-07-16
This paper re-examines how agent harness evolution is evaluated.
The author argues that many gains attributed to "self-evolving harnesses" might not actually come from the harness itself, but could merely be the result of iterative search. The paper points out:
- Harness evolution is essentially a form of task-feedback-based search, and should be compared against standard task searches under equivalent feedback and inference budgets.
- If the search process and the final test share the same benchmark, the results might indicate overfitting to that specific task set rather than reflecting a superior design.
The conclusion is that many existing improvements require more rigorous evaluation to be validated.
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