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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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.29682 |
| Publication time | Sat, 30 May 2026 03:34:45 +0000 |
| Retrieval time | 2026-05-30T03:41:55.382Z |
| Last seen | 2026-05-30T03:41:55.382Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | m9lOhtdehbFD |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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