Great paper from Meta on agent harness optimization.

Meta-Harness-style search uses one development set and one proposal policy, so every edit follows a single path and can get stuck in a local optimum.

This work splits the search into branches.

Each branch keeps the development cases its harnesses solve better than other branches, drops cases every branch already solves, and rewrites its own proposal policy from its history. A router then picks one branch's harness for each new input before it runs.

Relative to Meta-Harness, that gives +34.8% on Olympiad-level math, +11.6% on Terminal-Bench 2.0 and +3.8% on SWE-bench Lite. Harness selection and the router use development data only.

Paper: https://academy.dair.ai/papers/mixture-of-self-improving-branches-for-agent-harness-optimization-2609.37834