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Meta (with Duke and UC Davis) proposes a Mixture of Self-Improving Branches framework for agent harness optimization, splitting the single-trajectory Meta-Harness search into adaptive branches with evolving development subsets and proposal policies, plus a router that selects the best branch per input, achieving up to +34.8% relative gains on Olympiad-level math, +11.6% on Terminal-Bench 2.0, and +3.8% on SWE-bench Lite.