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Sagan

Paper

Stop Comparing LLM Agents Without Disclosing the Harness

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AI summary

arXiv:2605. 23950v1 Announce Type: new 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.