SearchOS-V1：迈向稳健的开放域信息检索智能体协作
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
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中文总结 AI 辅助
研究开放域信息检索智能体协作问题，提出SearchOS系统级多智能体框架，通过将搜索进展转化为显式状态、设计上下文管理、应用调度机制及引入中间件和技能系统，在相关数据集上领先基线，推动稳健信息检索协作。
中文摘要 AI 辅助
工具集成大语言模型的进展使网络搜索成为信息检索智能体的核心能力。但随着交互历史增长，智能体追踪任务进展困难，搜索失败会陷入重复循环。本文介绍SearchOS系统级多智能体框架，将脆弱、隐式搜索进展转化为显式、持久和共享状态。先将开放域信息检索表述为带基础引用的关系模式完成，设计面向搜索的上下文管理，在此基础上应用管道并行调度机制，还引入搜索工具中间件 harness 及可重用分层技能系统。在WideSearch和GISA上，SearchOS在评估的单智能体和多智能体基线中各项指标领先，为稳健信息检索协作铺平道路。
英文摘要
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state. First, we formulate open-domain information seeking as relational schema completion with grounded citations, where agents discover entities, populate attributes across linked tables, and anchor each value to source evidence. Then we design Search-Oriented Context Management (SOCM), which externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory. Built on SOCM, SearchOS applies a pipeline-parallel scheduling mechanism that overlaps the execution of sub-agents and continuously refills freed slots with tasks targeting unresolved coverage gaps to improve utilization and throughput. To schedule and control the execution of search agents, SearchOS introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion, and provides a reusable hierarchical skill system comprising strategy and access skills to augment the agents' search process and avoid repeating failed search patterns across runs. On WideSearch and GISA, SearchOS leads all metrics among the evaluated single- and multi-agent baselines, paving the way toward robust information-seeking collaboration.
发表机构
- Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
- Ant Group（蚂蚁集团）
机构由 AI 辅助整理，请以论文原文为准。
