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arXiv · 2512.23647

Nested Browser-Use Learning for Agentic Information Seeking

Baixuan Li·Jialong Wu·Wenbiao Yin·Kuan Li·Zhongwang Zhang·Huifeng Yin·Zhengwei Tao·Liwen Zhang·Pengjun Xie·Jingren Zhou·Yong Jiang
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Abstract

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.

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang. 2025-12-29. Nested Browser-Use Learning for Agentic Information Seeking. https://arxiv.org/abs/2512.23647

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