# SearchSwarm：面向长周期深度研究的代理大语言模型委托智能

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-08 08:00
- AIHOT 分数：62
- AIHOT 链接：https://aihot.virxact.com/items/cmq7jdn1l03odsl5wfk113fnc
- 原文链接：https://arxiv.org/abs/2606.09730

## AI 摘要

研究团队提出SearchSwarm，通过设计引导框架将长周期任务分解与委托决策编码为高质量轨迹，并用作监督微调数据，将委托智能内化到模型权重中。由此训练的SearchSwarm-30B-A3B模型在BrowseComp上达到68.1分，在BrowseComp-ZH上达到73.3分，均为同规模最佳。团队将开源引导框架、模型权重和训练数据。

## 正文

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.