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Jiaheng Zhang

Jiaheng Zhang has 3 papers in our library, in 2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Chenchen Jing, Chunhua Shen, Hao Chen.

3 papers in our library · 2026

OpenAlex · h-index 0 · 5 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10653

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

🎓National University of SingaporeSingapore · Singapore2025–2026 · 4 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksEvaluation & benchmarks · 1 workInterpretability · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models (2026 · 2 citations)
    Masked diffusion language models (MDLMs) generate text in parallel by gradually unmasking positions. They have two weaknesses. First, once a token is committed, it can never be revised. Second, the model predicts distributions for every position at each step but keeps only a few, so most of that work is thrown away.
  2. Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration (2026 · 1 citation)
    Diffusion language models (dLLMs) such as LLaDA generate text by iteratively unmasking tokens in no fixed order.
  3. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models (2026 · 1 citation)
    Test-time scaling improves LLM reasoning by sampling several candidate reasoning steps or full chains and keeping the most promising ones.

 Co-authors here

12 of 15 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 4 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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