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Cheng Tan

Cheng Tan has 2 papers in our library, in 2024–2026, most of them on Reasoning, Vision & language; often with Adam Fourney, Dongxu Zhang, Eduardo Salinas.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 11 · 35 works · 272 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 42 times in our library · ⭐ Paper Stars ranking: #9493

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

🎓Northeastern UniversityBoston · United States2023–2025 · 9 paperscurrent🎓Universiti Sains MalaysiaGeorge Town · Malaysia2023–2025 · 2 paperscurrent🎓Aerospace Information Research InstituteBeijing · China2025 · 1 papercurrent🏛️Chinese Academy of SciencesBeijing · China2025 · 1 papercurrent🎓New York UniversityNew York · United States2017–2019 · 3 papers🎓Shanghai Jiao Tong UniversityShanghai · China2012–2015 · 3 papers🎓Fudan UniversityShanghai · China2012–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks (2024 · 54 citations)
    General-purpose AI agents have to handle varied multi-step tasks, such as browsing the web, working with files, and writing and running code, rather than performing well in only one narrow domain.
  2. Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring (2026 · 3 citations)
    Long Chain-of-Thought (CoT) rationales make Multimodal Large Language Models (MLLMs) more accurate, but they also make them slow and memory-hungry.

 Co-authors here

9 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 42 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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