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arXiv AI By Wenxuan Ding, Qin Lu, Changlong Yu, Sha Li, Shuowei Jin, Xin Liu, Zhenyu Shi, Greg Durrett

Harness-Delta Attribution: Dissecting Gains From Harness Evolution

Read the original on arXiv AI →

The paper introduces Harness-Delta Attribution (HDA), a protocol that breaks down the performance gains from harness evolution into overfitting, test-time scaling, and residual generalizable improvement. Applying HDA to 11 executor models and three harness-evolution frameworks across various tasks, the study finds that over half of the search-set gains are due to overfitting or scaling, with limited transfer to held-out tasks. The results highlight the need for attribution-aware evaluation in future harness evolution research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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