Meta发布GitSwarm多智能体推理框架
Meta用Git仓库让数百智能体协作，解决复杂问题比单个智能体高14%准确率。
Meta Superintelligence Labs提出GitSwarm框架，通过共享Git仓库实现异步智能体协作。该框架使用GPT-5.5 Codex智能体，在IMOProofBench-Advanced基准上一次性解决全部30个问题。在ProgramBench测试中，500个智能体的得分为79.4%，显著高于单个智能体的65.1%。
Great paper from Meta Superintelligence Labs on compounding inference for long-horizon agents. One clear approach that's working with large multi-agent systems is persistent memory. Meta researchers describe a concrete approach for this in this paper. You'll find that inference-time compute usually goes to separate attempts, so most runs ignore partial work and failed ideas. Here is how they solve this problem: GitSwarm gives hundreds of asynchronous agents one shared Git repository. Each contribution is a commit that records which earlier commits, on any branch, it builds on. There is no central planner. Each worker decides whether to explore, verify, refine, or combine existing work. With GPT-5.5 Codex workers, it solves all 30 IMOProofBench-Advanced problems in one run. On ProgramBench, it scores 79.4%, against 65.1% for a single Codex agent prompted to keep going at a similar budget. Score keeps rising with more workers, from 73.1% at 100 to 79.4% at 500. Later workers build on 94.7% of published contributions, and 70.1% of failed research commits are cited by later accepted work. Paper: arxiv.org/abs/2610.04862 Chat with Paper: academy.dair.ai/papers/gitswar… 💬 9 🔄 0 ❤️ 5 👀 983 📊 8 ⚡
