Meta, Duke and UC Davis researchers unveil self-improving branches for agent harness optimization

Researchers from Meta, Duke University and the University of California, Davis have a new idea for making AI agents better. Leave the model alone and improve the scaffolding around it, using several self-improving teams instead of one. Their preprint, titled “Mixture of Self-Improving Branches for Agent Harness Optimization,” reports a 34.8% relative improvement on Olympiad-level math reasoning. That moved accuracy from 46.0% to 62.0% with the Gemini 3 Flash model, without anyone retraining the model itself. What a harness is, and why it matters More formally, an agent harness is the code framework wrapped around a large language model. It covers the prompts the model receives, the tools it can call, the context it gets to see and how its actions are executed. Harness optimization is the practice of searching for a better version of that wrapper automatically. The new paper, published on September 29, 2026 as arXiv:2609.37834v1, builds directly on an earlier system called Meta-Harness. How the branching approach works Meta-Harness, released March 30, 2026, had previously outperformed traditional methods across various benchmarks. The new work argues that a single search path leaves...