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Tong Zhu

Tong Zhu has 5 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Vision & language; often with Wenliang Chen, Xiaoye Qu, Yu Cheng.

5 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 20 works · 73 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 89 times in our library · ⭐ Paper Stars ranking: #6469

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

🎓Jiangsu UniversityZhenjiang · China2006–2026 · 3 paperscurrent🎓Beijing Jiaotong UniversityBeijing · China2010–2020 · 5 papers🎓Southeast UniversityNanjing · China2019 · 2 papers🏛️Ministry of RailwaysNew Delhi · India2010 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArtificial Intelligence · 5 worksArts and Humanities · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 3 worksSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 5 worksReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20252 papers20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training (2024 · 85 citations)
    Training sparse Mixture-of-Experts (MoE) language models from scratch needs a lot of data and can be unstable.
  2. Timo: Towards Better Temporal Reasoning for Language Models (2024 · 19 citations)
    Language models still reason poorly about time. Earlier fixes targeted narrow settings such as time-sensitive question answering, so they carry over badly to other temporal problems. This work looks for one framework that covers many kinds of temporal reasoning. The authors examine 38 subtasks from the Wang & Zhao…
  3. Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025 · 13 citations)
    Large language models can absorb personal details about real people during pretraining. This conflicts with legal rights such as the GDPR's Right To Be Forgotten, and retraining a model from scratch to remove such data is too expensive. The authors study machine unlearning (MU) as a cheaper alternative and build…
  4. NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models (2025 · 7 citations)
    NesTools tackles a gap in tool-learning evaluation for large language models: nested tool calls, where one tool's output feeds the parameters of a later call.
  5. Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale (2026 · 3 citations)
    Intern-S1-Pro, from Shanghai AI Laboratory, is presented as the first scientific multimodal foundation model with one trillion parameters.

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

Updated

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