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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 designed to improve the efficiency of open-domain information-seeking agents by making their search progress more explicit and persistent. It aims to prevent repetitive loops when initial searches fail, conserve search budgets, and enhance the quality and completeness of the final output. The system uses a multi-agent framework that includes Frontier Task, Evidence Graph, Coverage Map, Failure Memory, and Search Tool Middleware Harness.

What the Document Says

  • SearchOS is a system-level multi-agent framework for open-domain information seeking.
  • The system aims to convert implicit search progress into explicit, persistent, and shared state.
  • It defines open-domain information seeking as relational schema completion with grounded citations.
  • SearchOS has components: Frontier Task, Evidence Graph, Coverage Map, Failure Memory, Search Tool Middleware Harness.
  • The system uses a pipeline-parallel scheduling mechanism to overlap sub-agents' execution and fill freed slots with tasks targeting unresolved coverage gaps.
  • SearchOS leads in all metrics on WideSearch and GISA compared to single- and multi-agent baselines.

Key Concepts Mentioned

  • SearchOS
  • Open-domain information seeking
  • Relational schema completion
  • Frontier Task
  • Evidence Graph
  • Coverage Map
  • Failure Memory
  • Search Tool Middleware Harness
  • WideSearch
  • GISA

Claims Made by the Document

  • Recent advances in Tool-Integrated Large Language Models 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.
  • When searches fail, these systems can get trapped in repetitive loops, wasting search budgets and compromising the quality and completeness of the final output.

Evidence Used

  • Not stated in source

Methods / Approach

  • Formulating open-domain information seeking as relational schema completion with grounded citations
  • Designing Search-Oriented Context Management (SOCM)
  • Using 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 detailed information about the specific algorithms or techniques used within each component of SearchOS.
  • It is unclear how the system handles complex, ambiguous, or contradictory information.

Open Questions

  • How does SearchOS handle complex, ambiguous, or contradictory information?
  • What specific algorithms or techniques are used within each component of SearchOS?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper focuses on the design and performance of SearchOS, but it does not delve into the details of its inner workings.

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.