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Source note3 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-level multi-agent framework designed to improve the quality and completeness of search results in open-domain information-seeking tasks. The framework addresses the issue of tracking task progress as interaction histories grow by turning implicit search progress into explicit, persistent, and shared state.

What the Document Says

  • Recent advances have made web search a core capability of information-seeking agents.
  • As interaction histories grow, current single- and multi-agent systems can struggle to track task progress and become trapped in repetitive loops.
  • SearchOS is a system-level multi-agent framework that aims to improve the quality and completeness of search results.
  • Open-domain information seeking is formulated as relational schema completion with grounded citations.
  • Search-Oriented Context Management (SOCM) is designed, which externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory.
  • A pipeline-parallel scheduling mechanism is applied to improve utilization and throughput.
  • A Search Tool Middleware Harness is introduced to intercept model and tool interactions, record grounded evidence, and react to stalls or budget exhaustion.
  • SearchOS leads all metrics among the evaluated single- and multi-agent baselines on WideSearch and GISA.

Key Concepts Mentioned

  • open-domain information-seeking
  • search progress
  • multi-agent framework
  • relational schema completion
  • grounded citations
  • Search-Oriented Context Management (SOCM)
  • pipeline-parallel scheduling
  • Search Tool Middleware Harness

Claims Made by the Document

  • Recent advances have made web search a core capability of information-seeking agents.
  • Current single- and multi-agent systems can struggle to track task progress as interaction histories grow.
  • SearchOS aims to improve the quality and completeness of search results in open-domain information-seeking tasks.
  • SearchOS leads all metrics among the evaluated single- and multi-agent baselines on WideSearch and GISA.

Evidence Used

  • Not stated in source

Methods / Approach

  • formulating open-domain information seeking as relational schema completion
  • designing Search-Oriented Context Management (SOCM)
  • applying a pipeline-parallel scheduling mechanism
  • 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 paper does not provide specific details about the challenges faced by the authors during the development or testing of SearchOS.

Open Questions

  • What were the specific challenges encountered by the authors during the development and testing of SearchOS?
  • What are the potential drawbacks of the SearchOS approach?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on presenting the SearchOS framework and its results, but it does not discuss in detail the challenges or potential limitations.

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.