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Dongqi Fu

Dongqi Fu has 2 papers in our library, in 2026, most of them on Information retrieval & search; often with Hanghang Tong, Hanqing Zeng, Jiaru Zou.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 10 · 67 works · 309 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 3 times in our library · ⭐ Paper Stars ranking: #12204

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

🎓University of Illinois Urbana-ChampaignUrbana · United States2020–2026 · 21 paperscurrent🏢MetaMenlo Park · United States2024–2026 · 12 paperscurrent🏢Aqua Metrology SystemsSunnyvale · United States2026 · 1 papercurrent🎓University of RochesterRochester · United States2026 · 1 papercurrent🎓Beijing University of Posts and TelecommunicationsBeijing · China2016 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workPhysics and Astronomy · 1 workStatistical and Nonlinear Physics · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking (2026 · 3 citations)
    Many real questions can only be answered by combining facts spread over several tables, yet RAG systems mostly target plain text, and realistic benchmarks for multi-table retrieval are scarce.
  2. How to Make LMs Strong Node Classifiers? (2026)
    The paper asks whether standard, instruction-tuned text-to-text LMs can classify nodes in Text-Attributed Graphs (TAGs) as well as specialized graph models, without changing the LM's architecture.

 Co-authors here

11 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 3 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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