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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 a new system called SearchOS, a multi-agent framework designed to improve the efficiency and quality of open-domain information seeking for AI agents. The framework addresses the issue of tracking task progress as interaction histories grow, which can lead to repetitive loops and wasted search budgets. SearchOS does this by turning implicit search progress into explicit, persistent, and shared state through a system called Search-Oriented Context Management (SOCM). SOCM externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory. The paper reports that SearchOS outperforms other single- and multi-agent baselines on WideSearch and GISA benchmarks.

What the Document Says

  • The paper presents SearchOS, a system-level multi-agent framework designed to improve open-domain information seeking for AI agents.
  • The framework addresses the issue of tracking task progress as interaction histories grow and prevents repetitive loops by turning implicit search progress into explicit, persistent, and shared state through Search-Oriented Context Management (SOCM).
  • SearchOS externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory.
  • The paper states that SearchOS applies a pipeline-parallel scheduling mechanism to improve utilization and throughput by overlapping the execution of sub-agents and refilling freed slots with tasks targeting unresolved coverage gaps.
  • SearchOS introduces a Search Tool Middleware Harness to intercept model and tool interactions, record grounded evidence, and react to stalls or budget exhaustion.
  • The paper mentions a reusable hierarchical skill system for augmenting the agents' search process and avoiding repeating failed search patterns across runs.
  • SearchOS outperforms other single- and multi-agent baselines on WideSearch and GISA benchmarks.

Key Concepts Mentioned

  • AI agent
  • open-domain information seeking
  • search progress
  • Task
  • Evidence Graph
  • Coverage Map
  • Failure Memory
  • multi-agent framework
  • pipeline-parallel scheduling
  • Search Tool Middleware Harness
  • hierarchical skill system

Claims Made by the Document

  • SearchOS improves open-domain information seeking for AI agents.
  • SearchOS addresses the issue of tracking task progress as interaction histories grow.
  • The Search-Oriented Context Management (SOCM) turns implicit search progress into explicit, persistent, and shared state.

Evidence Used

  • The paper presents a system designed to improve open-domain information seeking for AI agents.
  • The text discusses the challenges faced by current single- and multi-agent systems in tracking task progress and avoiding repetitive loops.
  • The paper describes SearchOS's approach to externalizing the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory.

Methods / Approach

  • Formulating open-domain information seeking as relational schema completion with grounded citations
  • Designing Search-Oriented Context Management (SOCM)
  • Applying pipeline-parallel scheduling mechanism
  • Introducing a Search Tool Middleware Harness

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.