{"ID":2824449,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23647","arxiv_id":"2512.23647","title":"Nested Browser-Use Learning for Agentic Information Seeking","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.","short_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 interacti...","url_abs":"https://arxiv.org/abs/2512.23647","url_pdf":"https://arxiv.org/pdf/2512.23647v1","authors":"[\"Baixuan Li\",\"Jialong Wu\",\"Wenbiao Yin\",\"Kuan Li\",\"Zhongwang Zhang\",\"Huifeng Yin\",\"Zhengwei Tao\",\"Liwen Zhang\",\"Pengjun Xie\",\"Jingren Zhou\",\"Yong Jiang\"]","published":"2025-12-29T17:59:14Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.IR\",\"cs.MA\"]","methods":"[\"LoRA\"]","has_code":false}