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Yu Feng

Yu Feng has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Vision & language; often with Dan Roth, Haoyu Wang, Noah A. Smith.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 11 · 22 works · 447 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 136 times in our library · ⭐ Paper Stars ranking: #6391

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

🎓Chongqing University of Science and TechnologyChongqing · China2025–2026 · 4 paperscurrent🎓Dalian University of TechnologyDalian · China2008–2024 · 6 papers🎓Hebei University of TechnologyTianjin · China2022–2024 · 5 papers🎓Shandong UniversityJinan · China2021–2022 · 3 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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workEngineering · 1 workAerospace Engineering · 1 workAutomotive Engineering · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. BLINK: Multimodal Large Language Models Can See but Not Perceive (2024 · 206 citations)
    Popular multimodal benchmarks such as MMBench mostly test object recognition and language-side knowledge. They say little about whether models actually perceive visual structure. The authors present Blink, a benchmark built from 14 classic computer vision problems, including relative depth, visual and semantic…
  2. Constructing coherent spatial memory in LLM agents through graph rectification (2026 · 1 citation)
    LLMs can reason about a map described in a single prompt, but this breaks down for large environments that exceed the context window.

 Co-authors here

7 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 136 times (of the 149,439 papers whose bibliographies we read); 39 citing papers with a page here →.

Updated

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