智能体信息检索中的嵌套浏览器应用学习

摘要

信息检索（IS）智能体在各类广度和深度搜索任务中已展现出卓越性能，但其工具使用仍主要局限于API级别的片段检索和基于URL的页面获取，限制了通过真实浏览获取更丰富信息的能力。尽管完整的浏览器交互可解锁更深层能力，但其细粒度控制和冗长页面内容返回为ReAct式函数调用智能体带来了巨大复杂性。为弥合这一差距，我们提出嵌套式浏览器使用学习（NestBrowse），通过引入极简而完整的浏览器操作框架，采用嵌套结构将交互控制与页面探索解耦。该设计在实现高效深度网络信息获取的同时，简化了智能体推理过程。在具有挑战性的深度IS基准测试中的实证结果表明，NestBrowse在实践中具有显著优势。进一步的深度分析也印证了其高效性与灵活性。

English

Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet retrieval and URL-based page fetching, limiting access to the richer information available through real browsing. While full browser interaction could unlock deeper capabilities, its fine-grained control and verbose page content returns introduce substantial complexity for ReAct-style function-calling agents. To bridge this gap, we propose Nested Browser-Use Learning (NestBrowse), which introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure. This design simplifies agentic reasoning while enabling effective deep-web information acquisition. Empirical results on challenging deep IS benchmarks demonstrate that NestBrowse offers clear benefits in practice. Further in-depth analyses underscore its efficiency and flexibility.

智能体信息检索中的嵌套浏览器应用学习

Nested Browser-Use Learning for Agentic Information Seeking

摘要

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