It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers
A recent study challenges the assumption that higher-capability LLM models require less structural guidance. The research indicates that harness sensitivity is non-monotone across different model tiers, with some models performing better under stricter harness conditions. This suggests that optimal harness complexity may vary significantly depending on the model type and capabilities.
- ▪The study involved a controlled experiment with six models across four capability tiers and three harness conditions.
- ▪Results showed that increased harness verbosity can lower performance metrics for higher-capability models.
- ▪A strict harness achieved the highest performance for a reasoning model, contradicting previous assumptions about model capabilities.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26731 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
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Computer Science > Artificial Intelligence arXiv:2605.26731 (cs) [Submitted on 26 May 2026] Title:It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers Authors:Yong-eun Cho View a PDF of the paper titled It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers, by Yong-eun Cho View PDF HTML (experimental) Abstract:A prevalent assumption in LLM agent deployment holds that more structured harnesses universally improve reliability, and that higher-capability models need proportionally less structural guidance -- together implying a monotone inverse relationship between model capability tier and optimal harness complexity.
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