Scaling Laws for Agent Harnesses via Effective Feedback Compute
The article discusses a new approach to evaluating agent harnesses in language-model systems through Effective Feedback Compute (EFC). This method focuses on the quality of feedback rather than the quantity of computational resources used. The findings suggest that efficient feedback significantly improves task success rates compared to traditional metrics.
- ▪Effective Feedback Compute (EFC) credits feedback only when it is informative, valid, non-redundant, and retained for subsequent decisions.
- ▪EFC-based coordinates consistently predict failure rates better than raw-compute baselines and a strong multivariate SAS baseline.
- ▪Improving feedback quality can raise success rates from 0.27 to 0.90 while keeping raw cost and tool calls fixed.
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Computer Science > Computation and Language arXiv:2605.29682 (cs) [Submitted on 28 May 2026] Title:Scaling Laws for Agent Harnesses via Effective Feedback Compute Authors:Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che View a PDF of the paper titled Scaling Laws for Agent Harnesses via Effective Feedback Compute, by Xuanliang Zhang and 4 other authors View PDF HTML (experimental) Abstract:Agent harnesses increasingly determine the performance of language-model systems by deciding how models call tools, receive feedback, verify intermediate states, store memory, and revise solutions.
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