AI学会分身术：把大任务拆给小弟，自己只抓重点
大模型处理复杂任务时，上下文窗口是硬伤——信息一多就记不住。这篇论文让一个主模型当项目经理，把任务拆成小块，派给多个子模型去执行，子模型只返回精简结果，主模型就能省下脑容量专注决策。关键创新是：他们设计了一套流程，自动生成高质量的分工数据，再用这些数据微调模型，让模型学会什么时候该派活、怎么派、怎么整合结果。最终模型在深度搜索测试中拿了同规模最佳成绩。它不是你明天能用上的，但指向一个方向：未来的AI可能不是单打独斗的巨人，而是一群会协作的小兵。
📄 原文摘要(英文)
Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent's context budget. However, performing this well requires delegation intelligence: the ability to decompose complex tasks, determine when and what to delegate, and integrate returned results into the ongoing workflow. Training data for this capability is scarce in naturally occurring text, and to our knowledge, how to synthesize such data and train models to acquire this capability remains largely unexplored in the open-source community. To bridge this gap, we present a preliminary exploration targeting deep research, a representative long-horizon agent task. Specifically, we design a harness that guides the model toward high-quality task decomposition and delegation, while constraining subagents to return results properly to support the main agent's workflow. The harness-guided trajectories naturally encode correct delegation decisions, which we use as supervised fine-tuning data to internalize delegation intelligence into model weights. Our resulting model, SearchSwarm-30B-A3B, achieves 68.1 on BrowseComp and 73.3 on BrowseComp-ZH, the best results among all models of comparable scale. We will release our harness, model weights, and training data to facilitate future research.