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SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
arXiv cs.AI · 公開日 · 読了3分
30秒で要点
- 何が起きたか
- SearchOS-V1 is a multi-agent framework that tracks search progress explicitly to help AI agents find and cite information without getting stuck in repetitive loops.
- なぜ重要か
- Matters for engineers building information-seeking systems where multiple agents collaborate on web search and need to avoid wasted queries and incomplete results.
- 注意点
- Paper is recent academic work; real-world robustness beyond benchmark datasets WideSearch and GISA remains to be demonstrated in production environments.
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記事より
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Computer Science > Artificial Intelligence
arXiv:2607.15257v1 (cs)
[Submitted on 16 Jul 2026]
Title: SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
Authors: 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
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Abstract: 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
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