Stop Comparing LLM Agents Without Disclosing the Harness
The paper titled 'Stop Comparing LLM Agents Without Disclosing the Harness' argues that the performance of language model agents is more influenced by the execution harness than by the models themselves. It introduces the Binding Constraint Thesis, which suggests that harness configuration can lead to significant performance variances. The authors propose a new evaluation framework that emphasizes the need for transparency in harness specifications to avoid misleading comparisons.
- ▪The agent execution harness is a crucial factor in determining agent performance.
- ▪Small changes in harness configuration can lead to larger performance shifts than changing the model.
- ▪Current evaluation protocols may misattribute performance gains to model improvements rather than harness configurations.
2 outlets in our directory ran this story, first to last over 2 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23950 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| 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 | F5jHpBkVAAxt · 2 stories |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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 > Artificial Intelligence arXiv:2605.23950 (cs) [Submitted on 7 May 2026] Title:Stop Comparing LLM Agents Without Disclosing the Harness Authors:Yunbei Zhang, Janet Wang, Yingqiang Ge, Weijie Xu, Jihun Hamm, Chandan K. Reddy View a PDF of the paper titled Stop Comparing LLM Agents Without Disclosing the Harness, by Yunbei Zhang and 5 other authors View PDF HTML (experimental) Abstract:This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant of agent performance than the model it wraps.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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