arXiv: cs.AI preprintsInternational5 November 2026
HarnessFlow: MCTS-Guided Harness Optimization for LLM Agents
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arXiv:2611.06254v1 Announce Type: new Abstract: The performance of large language model (LLM) agents depends on both the underlying model and the harness that governs interaction with tools and the environment. Yet harness design remains largely manual, and automating it requires navigating an open-ended search space through costly end-to-end evaluations. We introduce HarnessFlow, a framework for optimizing complete executable harnesses under a limited candidate-evaluation budget, with the model and task environment held fixed. HarnessFlow organizes candidates and their evaluation histories in
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