Meta发布智能体优化新方法
Meta这篇论文改进了智能体优化框架，通过分支策略避免了局部最优，多项基准测试显著提升。
Meta提出分支式智能体优化方法，将搜索过程拆分为多个分支。每个分支保留其智能体能更好解决的案例，并基于自身历史重写提案策略。路由器为每个新输入选择最佳分支的智能体。该方法在Olympiad数学基准上比Meta-Harness提升34.8%，在Terminal-Bench 2.0上提升11.6%，在SWE-bench Lite上提升3.8%。
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://t.co/TkiZxIVEGM
