Stop Comparing LLM Agents Without Disclosing the Harness
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. We formalize and defend the Binding Constraint Thesis: in this regime, performance variance is governed more by harness configuration than by model choice, and current evaluation protocols therefore systematically misattribute harness-level gains to model improvements. We support this thesis along three lines. First, a control-th...
| Authors | Yunbei Zhang, Janet Wang, Yingqiang Ge et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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