💡 The paper introduces SearchOS, a system level multi agent framework that aims to improve the collaboration of information seeking agents in open domain search tasks. The problem addressed is that current single and multi agent systems struggle to track task progress as interaction histories grow, leading to repetitive loops, wasted search budgets, and compromised output quality.

To address this, the authors 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. They design Search Oriented Context Management, which externalizes the evolving state into a Frontier Task, an Evidence Graph, a Coverage Map, and a Failure Memory.

Built on this, SearchOS applies a pipeline parallel scheduling mechanism that overlaps the execution of sub agents and continuously refills free slots with tasks targeting unresolved coverage gaps to improve utilization and throughput. The system also 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.

The results show that SearchOS leads all metrics among the evaluated single and multi agent baselines on Wide Search and GISa, paving the way towards robust information seeking collaboration. The paper's contributions include the formulation of open domain information seeking as relational schema completion, the design of Search Oriented Context Management, and the introduction of a pipeline parallel scheduling mechanism and a Search Tool Middleware Harness, all of which work together to improve the collaboration of information seeking agents.