Nested Browser-Use Learning for Agentic Information Seeking
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.
69534a6d89916ff627aa3f4f
Baixuan Li
69534a6d89916ff627aa3f50
Jialong Wu
claimed_verified
2026-01-09T08:36:44.768Z
644a4fbc2166258fccc664bc
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
Jialong Wu
callanwu
user
callanwu
69534a6d89916ff627aa3f51
Wenbiao Yin
69534a6d89916ff627aa3f52
Kuan Li
69534a6d89916ff627aa3f53
Zhongwang Zhang
69534a6d89916ff627aa3f54
Huifeng Yin
69534a6d89916ff627aa3f55
Zhengwei Tao
69534a6d89916ff627aa3f56
Liwen Zhang
69534a6d89916ff627aa3f57
Pengjun Xie
69534a6d89916ff627aa3f58
Jingren Zhou
69534a6d89916ff627aa3f59
Yong Jiang
Nested Browser-Use Learning for Agentic Information Seeking
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.