AI搜索不再原地打转：新框架让多智能体协作不跑偏
现在的AI搜索助手有个通病：搜着搜着就忘了自己搜到哪了，反复搜同一个东西，浪费预算还漏信息。这篇论文设计了一个系统级框架SearchOS，核心是把搜索进度从AI的“脑子里”搬到“纸面上”——它用一张证据图、一张覆盖地图和一份失败记忆，让多个AI助手实时知道哪些信息已经找到、哪些还没搜、哪些路子走不通。然后像流水线一样调度：一个助手在搜A，另一个同时搜B，哪个搜完了立刻补上新的缺口。在WideSearch和GISA两个评测集上，所有指标都超过现有单智能体和多智能体方案。它不是你明天就能用的产品，但指明了方向：让AI搜索从“碰运气”变成“有账本”。
📄 原文摘要(英文)
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.