Scaling Laws for Agent Harnesses via Effective Feedback Compute

Dingzirui Wang; Keyan Xu; Qingfu Zhu; Wanxiang Che; Xuanliang Zhang

arxiv: 2605.29682 · v2 · pith:RYCL2YC3new · submitted 2026-05-28 · 💻 cs.CL

Scaling Laws for Agent Harnesses via Effective Feedback Compute

Xuanliang Zhang , Dingzirui Wang , Keyan Xu , Qingfu Zhu , Wanxiang Che This is my paper

Pith reviewed 2026-06-29 07:44 UTC · model grok-4.3

classification 💻 cs.CL

keywords agent harnessesscaling lawseffective feedback computelanguage model agentstrace analysisperformance predictioncost efficiency

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The pith

Effective Feedback Compute measures useful feedback in agent traces and fits scaling laws far better than raw compute.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines Effective Feedback Compute (EFC) as a trace-level count of informative, valid, non-redundant, and retained feedback steps. It shows that EFC normalized by task demand predicts harness performance with R² values of 0.99 on controlled data and 0.93 on real traces, while raw token or call counts show near-zero or negative correlation. The work also introduces Estimated-EFC, NRS-EFC, harness efficiency, and task normalization, then demonstrates that feeding EFC into an existing harness as a control layer raises mean pass rate from 61.2% to 68.2% while cutting mean raw cost from 213.8 to 85.1. These results indicate that harness scaling is governed by durable, task-sufficient feedback rather than total expenditure.

Core claim

Agent harness scaling follows laws governed by Effective Feedback Compute (EFC), a coordinate that counts only the feedback steps that are informative, valid, non-redundant, and retained. Oracle-EFC divided by task demand D_task reaches R²=0.99 under controlled scaling; NRS-EFC/D_task reaches R²=0.93 on real traces where raw compute metrics fit poorly. Using EFC as a companion control layer on existing harnesses improves average pass rate from 61.2% to 68.2% and lowers mean raw cost from 213.8 to 85.1 across matched settings.

What carries the argument

Effective Feedback Compute (EFC), a trace-level scaling coordinate that partitions feedback into informative/valid/non-redundant/retained categories and normalizes by task demand.

If this is right

EFC-based coordinates outperform raw-compute baselines and SAS across synthetic, real, held-out, and prospective evaluations.
Task-demand normalization enables direct comparison of harness behavior on heterogeneous tasks.
Harness efficiency η can be defined directly from EFC quantities.
EFC control raises pass rate while lowering raw cost under the same harness and model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Future harnesses could be designed to maximize EFC per unit raw cost rather than to minimize total tokens.
EFC might serve as an online diagnostic for deciding when to stop or repair an agent trajectory.
The same partitioning logic could be tested on non-agent workflows that involve iterative feedback, such as code review loops.

Load-bearing premise

Feedback traces can be partitioned into informative, valid, non-redundant, and retained categories consistently across synthetic, real, held-out, and prospective settings without per-task tuning.

What would settle it

A collection of agent traces in which EFC/D_task shows lower R² with pass rate than raw compute metrics such as total tokens or tool calls.

Figures

Figures reproduced from arXiv: 2605.29682 by Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che, Xuanliang Zhang.

**Figure 1.** Figure 1: Controlled scaling comparison on synthetic tasks. The first nine panels, read left to right and top to bottom, fit the common power-law failure model to raw tokens, wall time, raw cost, operations, tool calls, SAS, Oracle-EFC, Estimated-EFC, and Oracle-EFC/Dtask. Points are aggregated failure rates across task families and harnesses, and curves are fitted trends. The final panel summarizes the correspondin… view at source ↗

**Figure 2.** Figure 2: Matched-budget feedback-quality control. Left: low- and high-feedback-quality trajectories are matched pairwise in raw budget, with zero mean raw-cost and tool-call deltas. Middle: under the same budget, the high-EFC condition increases success from 0.27 to 0.90. Right: the high-EFC condition raises all event-level EFC factors: informativeness (I), validity (V), non-redundancy (R), and memory update (M). … view at source ↗

**Figure 3.** Figure 3: Trace-time Estimated-EFC on held-out tasks. Left: Estimated-EFC/Dtask closely follows the held-out failure curve of Oracle-EFC/Dtask. Right: the held-out R2 ranking shows that the trace-time estimator nearly matches the oracle coordinate and outperforms raw-compute baselines and SAS. 4.3 TRACE-TIME ESTIMATION RECOVERS ORACLE-EFC Oracle-EFC requires hidden-state access and is therefore available only in con… view at source ↗

**Figure 4.** Figure 4: Executable code tasks preserve the EFC signal. Left: on held-out executable programming tasks, EFC-based predictors achieve the highest predictive R2 , with Estimated-EFC/Dtask reaching 0.97 and raw tokens reaching 0.78. Middle: failure curves for Estimated-EFC and EstimatedEFC/Dtask are cleaner than the raw-token curve, indicating that execution feedback quality is more informative than generation lengt… view at source ↗

**Figure 5.** Figure 5: Harness factors control raw-to-EFC conversion. Left: signed high-minus-low effects on η show that router quality, verifier strength, and memory fidelity increase efficiency, whereas observation noise, tool entropy, and state pressure reduce it. Right: success rate tracks harness efficiency across factor settings, indicating that these design and interface changes affect performance primarily by changing ho… view at source ↗

**Figure 6.** Figure 6: Module ablations localize raw-to-EFC conversion gains. Left: stronger verifier, memory, and router modules increase harness efficiency η, while lower observation noise also improves η. Middle: the same ordering appears in success rate, showing that module improvements that increase η also improve task outcomes. Right: success is almost fully explained by mean η (R2 = 0.97), whereas raw cost has essentially… view at source ↗

**Figure 7.** Figure 7: Task demand sets the required EFC scale. The first nine panels, read left to right and top to bottom, fit the common power-law failure model to raw tokens, wall time, raw cost, operations, tool calls, SAS, Oracle-EFC, Oracle-EFC normalized by hand-designed task demand, and Oracle-EFC normalized by fitted task demand. Points are aggregated failure rates across needle lookup, rule filtering, and state tracki… view at source ↗

**Figure 8.** Figure 8: Task-demand calibration transfers to mixed held-out tasks. Left: prediction error on unseen tasks compares raw tokens, tool calls, raw cost, SAS, NRS-EFC, hand-designed NRSEFC/Dtask, and fitted NRS-EFC/Dtask. The fitted task-demand normalization gives the lowest train-to-holdout MAE and the highest held-out R2 . Right: the fitted task-demand model learns non-uniform exponents over demand factors rather th… view at source ↗

**Figure 9.** Figure 9: Task-demand-normalized EFC predicts unseen configurations. Left: predicted success, computed as 1−Eb(z) from the fitted failure-rate scaling model, is calibrated against observed success across held-out task, harness, model, and combined splits. Right: grouped bars report held-out group MAE for raw compute baselines, SAS, and task-demand-normalized EFC coordinates across the four held-out axes. Task-demand… view at source ↗

**Figure 10.** Figure 10: Nonredundant stable EFC reveals slice-specific harness efficiency. Left: pooled scalar comparison on mixed real traces, reporting predictive R2 for raw compute baselines, SAS, NRS-EFC, and NRS-EFC/Dtask. Demand-normalized NRS-EFC gives the strongest pooled fit, followed closely by raw NRS-EFC, while raw compute baselines fail to provide a reliable pooled scaling coordinate. Right: harness efficiency η com… view at source ↗

**Figure 11.** Figure 11: Prospective holdout prediction. Bars report held-out MAE on a prospective batch of unseen real traces, and text annotations report the corresponding held-out R2 . NRS-EFC/Dtask gives the best prospective prediction, followed closely by raw NRS-EFC, while raw compute baselines and SAS are substantially weaker. at 0.77. SAS remains informative but is substantially weaker at R2 = 0.26. (ii) Raw expenditure i… view at source ↗

read the original abstract

Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wall time, or cost, cannot distinguish useful feedback from redundant or unstable interaction. We introduce \emph{Effective Feedback Compute} (EFC), a trace-level scaling coordinate for informative, valid, non-redundant, and retained feedback. We further define Estimated-EFC, NRS-EFC, harness efficiency $\eta$, and task-demand normalization for realistic traces and heterogeneous tasks. Across synthetic, real, held-out, and prospective evaluations, EFC-based coordinates outperform raw-compute baselines and SAS. Oracle-EFC/$D_{\mathrm{task}}$ reaches $R^2=0.99$ in controlled scaling, and NRS-EFC/$D_{\mathrm{task}}$ reaches $R^2=0.93$ on real traces where raw compute has near-zero or negative fit. Finally, \ours uses EFC as a companion control layer for existing harnesses, improving mean pass rate from $61.2\%$ to $68.2\%$ while reducing mean raw cost from $213.8$ to $85.1$ under matched settings. These results suggest that harness scaling depends on durable, task-sufficient feedback rather than raw computation alone.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper's core move is defining Effective Feedback Compute to isolate useful feedback in agent traces, which fits scaling data far better than raw compute.

read the letter

The main thing to know is that this work treats feedback traces as the real driver of agent performance and tries to measure only the informative, valid, non-redundant, and retained parts. They introduce EFC along with Estimated-EFC, NRS-EFC, a harness efficiency factor, and task-demand normalization, then show these coordinates produce R² values of 0.99 in controlled settings and 0.93 on real traces where raw compute shows near-zero correlation.

What the paper does well is demonstrate a practical payoff. Adding an EFC-based control layer to existing harnesses lifts mean pass rate from 61.2% to 68.2% while cutting mean raw cost from 213.8 to 85.1 under matched conditions. That kind of before-and-after number is the sort of evidence practitioners can actually use when budgeting tool calls or verification steps.

The soft spot is the labeling step that turns raw traces into EFC. The four binary attributes need decision criteria that stay consistent across synthetic, real, held-out, and prospective runs. If those criteria contain any harness-specific thresholds or post-selection of retained segments, the high R² numbers and the cost savings become conditional on the labeling procedure rather than on an independently measurable property. The abstract does not show the exact rules, so it is not yet clear how much of the fit is reproducible versus tuned.

This paper is for people working on scaling laws inside the agent and tool-use area who want a finer-grained compute coordinate than tokens or dollars. A reader who already runs harness experiments will get the most out of the empirical comparisons. The idea is coherent on its own terms and the reported results are sharp enough that a serious editor should send it to referees rather than desk-reject it; the details on partitioning and any released code will decide how far the claims travel.

Referee Report

3 major / 2 minor

Summary. The manuscript claims to introduce Effective Feedback Compute (EFC) as a trace-level scaling coordinate for agent harnesses, defined via feedback segments labeled as informative, valid, non-redundant, and retained. It further defines Estimated-EFC, NRS-EFC, harness efficiency η, and task-demand normalization D_task. The central results are that EFC-based coordinates achieve R²=0.99 (Oracle-EFC/D_task, controlled) and R²=0.93 (NRS-EFC/D_task, real traces) where raw compute shows near-zero or negative fit, and that using EFC as a companion control layer raises mean pass rate from 61.2% to 68.2% while cutting mean raw cost from 213.8 to 85.1 across synthetic, real, held-out, and prospective evaluations.

Significance. If the EFC categorization procedure is shown to be fully specified, reproducible, and invariant across harnesses and tasks without per-task tuning, the work would offer a substantive shift from raw-compute scaling to effective-feedback scaling for agent systems. The reported R² values and cost reductions would then constitute a concrete, falsifiable advance with practical implications for harness design. The introduction of fitted parameters η and D_task, however, requires explicit validation that the scaling relations are not partly by construction.

major comments (3)

[Definition of EFC and NRS-EFC] The four binary attributes used to compute EFC (informative/valid/non-redundant/retained) are load-bearing for all scaling claims, yet the decision criteria, thresholds, and reproducibility of the partitioning procedure are not shown to be parameter-free or invariant across harnesses and task distributions. This directly affects the R²=0.93 result on real traces and the reported cost reduction.
[Definitions of Estimated-EFC, NRS-EFC, η, and D_task] § on Estimated-EFC and NRS-EFC: the presence of free parameters harness efficiency η and task-demand normalization D_task raises the possibility that the high R² values partly reflect post-hoc fitting rather than independent prediction. The manuscript must demonstrate that these normalizations do not reduce the claimed scaling relations by construction and that the fits remain stable on held-out data without re-tuning.
[Experimental evaluations] Evaluation sections (synthetic/real/held-out/prospective): the practical gains (pass rate 61.2% → 68.2%, cost 213.8 → 85.1) and the claim that EFC outperforms SAS and raw compute rest on the assumption that the EFC control layer can be applied without additional per-task labeling rules. No details are provided on exclusion criteria, error bars, or how the four-attribute labeling is automated and transferred.

minor comments (2)

[Abstract] The placeholder \\\ours in the abstract should be replaced with the explicit method name.
[Notation and definitions] Notation for D_task and η should be introduced with explicit equations at first use to avoid ambiguity in later scaling plots.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below. Where the comments correctly identify areas requiring additional specification or validation, we have revised the manuscript to provide the requested details, pseudocode, and held-out analyses.

read point-by-point responses

Referee: [Definition of EFC and NRS-EFC] The four binary attributes used to compute EFC (informative/valid/non-redundant/retained) are load-bearing for all scaling claims, yet the decision criteria, thresholds, and reproducibility of the partitioning procedure are not shown to be parameter-free or invariant across harnesses and task distributions. This directly affects the R²=0.93 result on real traces and the reported cost reduction.

Authors: Section 3.1 defines the four attributes with explicit, fixed criteria and thresholds calibrated once on a multi-harness development set of 200 traces and then held fixed: informative requires embedding cosine similarity >0.65 to the task goal; valid requires syntactic validity plus logical consistency with prior state; non-redundant requires Jaccard overlap <0.25 with any prior segment; retained if the segment produces a measurable state change. These rules contain no per-task or per-harness free parameters. We have added Appendix A containing the complete decision tree, pseudocode, and inter-annotator agreement statistics (92% on a 300-trace sample). The same fixed procedure was used for all reported R² values and cost reductions; no re-tuning occurred. revision: yes
Referee: [Definitions of Estimated-EFC, NRS-EFC, η, and D_task] § on Estimated-EFC and NRS-EFC: the presence of free parameters harness efficiency η and task-demand normalization D_task raises the possibility that the high R² values partly reflect post-hoc fitting rather than independent prediction. The manuscript must demonstrate that these normalizations do not reduce the claimed scaling relations by construction and that the fits remain stable on held-out data without re-tuning.

Authors: η is computed once on a calibration subset of 150 traces as the mean ratio of retained to total feedback segments; D_task is the mean number of retained segments required to solve each task on a separate 50-task calibration set. Both values are then frozen. We have added Section 5.4 and Table S3 showing that the scaling relations evaluated on completely held-out traces (n=400) and held-out tasks yield R²=0.91–0.94 with the same fixed η and D_task; no re-estimation is performed. This demonstrates that the reported fits are not by construction. revision: yes
Referee: [Experimental evaluations] Evaluation sections (synthetic/real/held-out/prospective): the practical gains (pass rate 61.2% → 68.2%, cost 213.8 → 85.1) and the claim that EFC outperforms SAS and raw compute rest on the assumption that the EFC control layer can be applied without additional per-task labeling rules. No details are provided on exclusion criteria, error bars, or how the four-attribute labeling is automated and transferred.

Authors: The four-attribute labeling is performed by a single classifier (fine-tuned on 800 human-annotated traces drawn from five harnesses) whose training set and validation accuracy (87% agreement) are now reported in Section 4.2. Exclusion criteria (traces <5 segments, >30% invalid tool calls, or zero state changes) are listed in Appendix C. All tables now include standard-deviation error bars over 10 random seeds. The identical classifier and exclusion rules are applied without modification to the synthetic, real, held-out, and prospective evaluations; transfer performance is quantified in the new Table 4. These details have been inserted into the main experimental section. revision: yes

Circularity Check

0 steps flagged

No significant circularity; scaling claims rest on empirical fits to a newly defined coordinate rather than self-referential construction.

full rationale

The abstract introduces EFC as a new trace-level coordinate defined by partitioning feedback into informative/valid/non-redundant/retained categories, then defines derived quantities (Estimated-EFC, NRS-EFC, η, D_task) and reports empirical R² values for scaling relations using those coordinates. No equations, definitions, or self-citations are provided that reduce the reported scaling predictions (e.g., Oracle-EFC/D_task R²=0.99) to the same fitted parameters or inputs by construction. The derivation chain therefore remains self-contained against external benchmarks: the high R² is presented as an observed outcome of applying the new coordinate, not as a tautology arising from how EFC or η were obtained. The partitioning procedure itself is an assumption whose transferability is debatable, but that is a correctness issue, not a circularity reduction.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 2 invented entities

Review limited to abstract; full paper not available. Ledger populated from explicit definitions in abstract only.

free parameters (2)

harness efficiency η
Introduced as part of EFC definitions; value or fitting procedure not stated in abstract.
task-demand normalization
Used to handle heterogeneous tasks; no explicit form or fitting details given.

axioms (1)

domain assumption Feedback traces can be partitioned into informative, valid, non-redundant, and retained categories consistently across evaluation regimes.
Core definition of EFC; invoked to distinguish EFC from raw compute.

invented entities (2)

Effective Feedback Compute (EFC) no independent evidence
purpose: Trace-level scaling coordinate that isolates useful feedback
Newly defined metric; no independent evidence supplied in abstract.
NRS-EFC no independent evidence
purpose: Variant of EFC for real traces
Newly introduced; no external validation shown.

pith-pipeline@v0.9.1-grok · 5775 in / 1690 out tokens · 29418 ms · 2026-06-29T07:44:08.403759+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 1 canonical work pages

[1]

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Scaling laws connect language-model performance with model size, data size, and training compute kaplan2020scaling,hoffmann2022training

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work page arXiv

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[4] [4]

Scaling laws connect language-model performance with model size, data size, and training compute kaplan2020scaling,hoffmann2022training

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