多智能体协作搜索老是卡壳？论文提出SearchOS，用显式状态管理和管道并行调度解决重复循环，在WideSearch和GISA上全面领先。搞检索智能体可以看看。
SearchOS是一个系统级多智能体协作框架，旨在解决工具集成大语言模型在信息检索中的重复搜索和预算浪费问题。它通过关系型模式补全和基于引用的证据填充，将隐式搜索进度显式化，并设计了Search-Oriented Context Management (SOCM) 来管理进化状态，包括Frontier Task、Evidence Graph、Coverage Map和Failure Memory。在WideSearch和GISA两个基准上，SearchOS在所有评估指标上均领先于单智能体和多智能体基线模型。
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
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.