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Tianyu Zhang

Tianyu Zhang has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Dialogue systems, Reasoning; often with An Zhang, Baolong Bi, Chen Chen.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 10 · 39 works · 262 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 36 times in our library · ⭐ Paper Stars ranking: #8101

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 Electronic Science and Technology of ChinaChengdu · China2019–2025 · 26 paperscurrent🎓Chinese Academy of Medical Sciences & Peking Union Medical CollegeBeijing · China2024 · 3 papers🎓Dalian University of TechnologyDalian · China2022–2023 · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 1 workDialogue systems · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Scaling Latent Reasoning via Looped Language Models (2025 · 22 citations)
    Most large language models learn to reason through explicit chain-of-thought added after pre-training. This lengthens outputs and makes little use of pre-training data. The authors propose Ouro, a family of Looped Language Models (LoopLM) in which a stack of weight-tied layers is applied several times, so hidden…
  2. Neuron-Level Sequential Editing for Large Language Models (2025 · 15 citations)
    Large language models store facts that go out of date, and full retraining is expensive. Parameter-editing methods such as ROME and MEMIT work well for a single round of edits. When edits are applied one after another, however, they tend to lose earlier edits ("model forgetting") and can collapse into incoherent…
  3. Chronological Thinking in Full-Duplex Spoken Dialogue Language Models (2026 · 3 citations)
    Full-duplex spoken dialogue language models (SDLMs) listen and speak at the same time. While the user talks, though, current systems just emit a stream of silence tokens. This wastes the listening period and may push an autoregressive model toward degenerate repetition. The authors propose CT-SDLM (implemented as…

 Co-authors here

12 of 17 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 36 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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