[2605.29682] Scaling Laws for Agent Harnesses via Effective Feedback Compute

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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<br>View a PDF of the paper titled Scaling Laws for Agent Harnesses via Effective Feedback Compute, by Xuanliang Zhang and 4 other authors

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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. Yet current test-time scaling analyses often parameterize this process by raw expenditure -- tokens, tool calls, operations, wall time, or cost -- which does not distinguish useful feedback from redundant or unstable interaction. We introduce \emph{Effective Feedback Compute} (EFC), a trace-level scaling coordinate that credits feedback only when it is informative, valid, non-redundant, and retained for subsequent decisions, and we normalize it by task demand when comparing tasks with different feedback requirements. Across synthetic controllable tasks, executable code tasks, real benchmark traces, held-out splits, and a prospective validation batch, EFC-based coordinates consistently predict failure rates better than raw-compute baselines and a strong multivariate SAS baseline. In controlled scaling, raw tokens and tool calls explain limited variation ($R^2=0.33$ and $0.42$), SAS reaches $0.88$, while Oracle-EFC and Estimated-EFC reach $0.94$ and Oracle-EFC/$D_{\mathrm{task}}$ reaches $0.99$. Matched-budget interventions show that improving feedback quality raises success from $0.27$ to $0.90$ while raw cost and tool calls are fixed. On mixed real traces, NRS-EFC/$D_{\mathrm{task}}$ reaches $R^2=0.92$ while raw compute has near-zero or negative fit, and it remains the best predictor in a prospective holdout ($R^2=0.85$). These results suggest that harness scaling is governed less by how much computation is spent than by how efficiently raw budget is converted into durable, task-sufficient feedback.

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Computation and Language (cs.CL)

Cite as:<br>arXiv:2605.29682 [cs.CL]

(or<br>arXiv:2605.29682v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.29682

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Xl Zhang [view email]<br>[v1]<br>Thu, 28 May 2026 09:45:47 UTC (422 KB)

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