Runnan Fang
Runnan Fang has 4 papers in our library, in 2025–2026; often with Pengjun Xie, Fei Huang, Jialong Wu.
OpenAlex · h-index 3 · 29 works · 62 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 115 times in our library · ⭐ Paper Stars ranking: #6353
Affiliations
🎓Zhejiang UniversityHangzhou ·
China2024–2026 · 16 papers🏢Alibaba GroupHangzhou ·
China2024–2026 · 8 papers🏢Alibaba GroupGeorge Town ·
Cayman Islands2025–2026 · 4 papers🎓Westlake UniversityHangzhou ·
China2026 · 3 papers🏢TencentShenzhen ·
China2026 · 1 paper
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 3 worksComputer Vision and Pattern Recognition · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Inverted index · 1 work
Works by year
2020s
Papers
- WebWalker: Benchmarking LLMs in Web Traversal (2025 · 72 citations)
Search-engine retrieval in standard RAG pipelines tends to return surface-level pages. Answers buried several clicks deep inside a website are often missed. The authors call this problem Web Traversal: starting from a site's root URL, an agent must click through subpages to find the information a query needs. To… - WebDancer: Towards Autonomous Information Seeking Agency (2025 · 50 citations)
WebDancer addresses how to train, from scratch, an open web agent that can carry out the long, multi-step research associated with systems like Deep Research. - OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025 · 14 citations)
LLM-based long-form writing usually depends on retrieval-augmented generation, but standard retrieval tends to return shallow, repetitive, and unoriginal material. - U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents (2026)
LLM agents that call tools during long conversations with users quickly run out of context. Existing context-folding methods address this by compressing earlier history, but they were designed for tasks with one goal. In multi-turn, user-facing dialogues they fail in two ways: they permanently drop small but decisive…
Co-authors here
- Pengjun Xie ×4
- Fei Huang ×3
- Jialong Wu ×3
- Wenbiao Yin ×3
- Yong Jiang ×3
- Zekun Xi ×3
- Ningyu Zhang ×2
- Deyu Zhou
- Dingchu Zhang
- Huajun Chen
- Linhai Zhang
- Liwen Zhang
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2025
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2025
Cited by
Updated
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