Kuan Li
2026
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 | Wentao Zhang | Zhiqiang Gao
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Baixuan Li | Jialong Wu | Wenbiao Yin | Kuan Li | Zhongwang Zhang | Huifeng Yin | Zhengwei Tao | Liwen Zhang | Pengjun Xie | Jingren Zhou | Yong Jiang | Wentao Zhang | Zhiqiang Gao
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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.
2010
SRL-Based Verb Selection for ESL
Xiaohua Liu | Bo Han | Kuan Li | Stephan Hyeonjun Stiller | Ming Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Xiaohua Liu | Bo Han | Kuan Li | Stephan Hyeonjun Stiller | Ming Zhou
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing