SearchOS: 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 , a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state. formulates open-domain information seeking as relational schema completion with grounded citations, where agents discover entities, record attributes across linked tables, and anchor each value to source evidence. Inspired by this paradigm, we design three core modules of : (1) Search-Oriented Context Management (SOCM), which externalizes and shares evolving search state across sub-agents and supports pipeline-parallel scheduling to improve utilization and throughput; (2) a Search Tool Middleware Harness, which intercepts model and tool interactions to record grounded evidence and respond to stalls or budget exhaustion; and (3) a reusable hierarchical skill system, which provides strategy and access skills to guide task-level search and source-specific retrieval. On WideSearch and GISA, leads all metrics among the evaluated single- and multi-agent baselines, paving the way toward robust information-seeking collaboration.