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Source note4 min read

SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

Source

arXiv

arxiv.org/abs/2607.15257v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Yuyao Zhang, Junjie Gao, Zhengxian Wu, Jiaming Fan, Jin Zhang, Shihan Ma, Yao Yao, Weiran Qi, Chuyan Jin, Guiyu Ma, Xingzhong Xu, Kai Yang, Ji-Rong Wen, Zhicheng Dou
  • Venue: arXiv: cs.AI
  • Published: 2026-07-16
  • Collected: 2026-07-17
  • URL: http://arxiv.org/abs/2607.15257v1
  • PDF: https://arxiv.org/pdf/2607.15257v1
  • DOI: UNKNOWN

Plain-Language Brief

The paper introduces SearchOS, a system that aims to improve the efficiency of open-domain information seeking for information-seeking agents. It struggles with tracking task progress as interaction histories grow and fails to yield useful evidence, leading to repetitive loops and compromising the quality of the final output. SearchOS turns implicit search progress into explicit, persistent, and shared state by formulating open-domain information seeking as relational schema completion with grounded citations, and designs a Search-Oriented Context Management (SOCM) to manage the evolving state.

What the Document Says

  • Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents.
  • 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.
  • The paper introduces SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state.
  • SearchOS formulates open-domain information seeking as relational schema completion with grounded citations.
  • SearchOS designs Search-Oriented Context Management (SOCM) to externalize the evolving state.
  • SOCM includes Frontier Task, Evidence Graph, Coverage Map, and Failure Memory.
  • SearchOS applies a pipeline-parallel scheduling mechanism that overlaps the execution of sub-agents.
  • SearchOS introduces a Search Tool Middleware Harness to record grounded evidence and react to stalls or budget exhaustion.
  • The paper evaluates SearchOS on WideSearch and GISA, leading all metrics among the evaluated single- and multi-agent baselines.

Key Concepts Mentioned

  • Information-seeking agents
  • Web search
  • Task progress
  • Relational schema completion
  • Grounded citations
  • Search-Oriented Context Management (SOCM)
  • Frontier Task
  • Evidence Graph
  • Coverage Map
  • Failure Memory
  • Pipeline-parallel scheduling
  • Search Tool Middleware Harness
  • WideSearch
  • GISA

Claims Made by the Document

  • Recent advances have made web search a core capability of information-seeking agents.
  • As interaction histories grow, agents struggle to track task progress.
  • Current single- and multi-agent systems can become trapped in repetitive loops when search attempts fail.
  • SearchOS turns implicit search progress into explicit, persistent, and shared state.
  • SearchOS formulates open-domain information seeking as relational schema completion with grounded citations.
  • SearchOS leads all metrics among the evaluated single- and multi-agent baselines on WideSearch and GISA.

Evidence Used

  • Not stated in source

Methods / Approach

  • Designing a system-level multi-agent framework (SearchOS)
  • Formulating open-domain information seeking as relational schema completion with grounded citations
  • Designing Search-Oriented Context Management (SOCM) to externalize the evolving state
  • Applying pipeline-parallel scheduling mechanism to overlap the execution of sub-agents
  • Introducing a Search Tool Middleware Harness

Limitations Stated by the Authors

  • Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The provided text lacks information on the limitations of the presented method.

Open Questions

  • What are the specific details of the pipeline-parallel scheduling mechanism in SearchOS?
  • What is the hierarchical skill system comprising strategy and access skills in the Search Tool Middleware Harness?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on presenting the new framework (SearchOS) for improving the efficiency of open-domain information seeking. The text does not provide sufficient details about the specifics of the pipeline-parallel scheduling mechanism and the hierarchical skill system.
  • The evaluation is based on WideSearch and GISA, but it's unclear what these datasets contain or their significance.

Raw Abstract / Excerpt

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.