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Ruixue Ding

Ruixue Ding has 2 papers in our library, in 2025, most of them on Reasoning, Information retrieval & search; often with Feng Zhao, Pengjun Xie, Qiuchen Wang.

2 papers in our library · 2025

OpenAlex · ORCID · Wikidata · h-index 5 · 18 works · 266 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 89 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7503

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Alibaba GroupHangzhou · China2019–2026 · 13 paperscurrent🏢Alibaba GroupGeorge Town · Cayman Islands2026 · 3 paperscurrent🎓Beihang UniversityBeijing · China2018–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents (2025 · 39 citations)
    The paper addresses retrieval-augmented generation (RAG) over large collections of visually rich documents such as slides with charts, tables and diagrams.
  2. VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning (2025 · 23 citations)
    Answering questions over visually rich documents such as slides, reports, charts and tables is difficult for standard RAG.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. A Neural Multi-digraph Model for Chinese NER with Gazetteers ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetRuixue Ding et al.2019 · 144 citations
  2. Better Modeling of Incomplete Annotations for Named Entity Recognition ↗Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · no summary yetZhanming Jie et al.2019 · 110 citations
  3. ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning AgentsConference on Empirical Methods in Natural Language Processing (EMNLP)Qiuchen Wang, Ruixue Ding, Zehui Chen, et al.2025 · 39 citations
  4. VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement LearningConference on Neural Information Processing Systems (NeurIPS)Qiuchen Wang, Ruixue Ding, Yu Zeng, et al.2025 · 23 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 89 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ruixue-ding/