AI团队从1人扩到1024人，效率真能涨
多智能体系统一直有个瓶颈：一个中央调度员指挥所有AI干活，人一多调度员自己先卡死。这篇论文干脆把调度员撤了，让每个AI自己认领任务、互相通报进度、自己验证结果，像一群自由职业者自发组队。结果在编程任务上，把AI从1个扩到128个，通过率从19.31%涨到28.78%;在另一个任务上扩到1024个，通过率从33.89%涨到55.06%。更关键的是，规模越大，AI之间还自发演化出了分工和协作模式。这不是你明天能用的产品，但它指出了一个方向：以后提升AI能力，除了堆参数，还可以堆数量——让一群AI自己组织起来干活。
📄 原文摘要(英文)
A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers. To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator: concurrent workers execute a multi-agent cooperation loop, continuously gathering context, claiming and self-assigning sub-tasks, taking action and sharing findings, verifying results, and merging progress in an asynchronous manner. The loop is supported by the agentic organization infrastructure comprising three components: a shared workspace holds proposed, ongoing, and completed work; a message interface lets workers communicate; and shared context retains reusable findings and work intentions. To test the scalability of Agensh, we evaluate it on the five hardest ProgramBench tasks with GPT-5.6-sol (high). Scaling from 1 to 128 agents raises the mean final test-pass rate from 19.31% to 28.78%, an approximately 49% relative improvement. Larger organizations reach comparable test-pass rates earlier. On pandoc, scaling from 1 to 1,024 agents raises the final test-pass rate from 33.89% to 55.06%. Worker trajectories further show that different forms of self-organized cooperation gradually emerges and standardizes as the organization grows. These results reveal the number of agents as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets.