AI 团队没有老板，1024 个智能体自己抢活干
现在的多智能体系统都有一个中央调度员，它负责给每个 AI 分活、协调进度——但调度员本身成了瓶颈，人一多就卡。这篇论文干脆把调度员撤了：1024 个 AI 在一个共享工作区里自己认领任务、做完贴结果、别人看到再接着做，像一群没有项目经理的工程师在 GitHub 上自发协作。结果很直接：任务越难，人越多越有效，在某个编程任务上从 1 个 AI 的 33.9% 成功率涨到 1024 个的 55.1%。更值得注意的是，随着规模变大，这群 AI 自己演化出了分工模式——有人专门验证、有人专门查资料，没人指挥，秩序自己长出来。这不是你明天能用上的工具，但它指出了一个方向：AI 的能力可能不再只靠单个模型变强，靠堆数量也能堆出新的智能。
📄 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.