Offloop用D1调度器解决了多智能体互相重复工作的老问题，成本低还让你用自己订阅的AI。
多智能体系统面临智能体重复工作、互相通信浪费token的难题。Offloop团队训练了一个名为D1的调度器模型，它能决定哪个智能体下一步行动，并在合适时保持静默。该方法在GDPval基准上达到了SOTA性能，同时大幅降低了成本。用户还可以自带自己的AI订阅。
The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, ...
The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate work and burn tokens talking to each other. Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost. You can bring your own AI subscription. offloop.org Your browser does not support the video tag. 🔗 View on Twitter Offloop @Offloop Introducing Offloop! We're a team of four. Today our multi-agent harness hit state of the art on GDPval, ahead of Claude code and Codex across jobs that pay $2.4 trillion a year in the US. Offloop gives every knowledge worker what the Fortune 500 spends billions on: a high-performing agent army that runs itself and grows the business. Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 0 ❤️ 11 👀 2609 📊 6 ⚡