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Ermo Hua

Ermo Hua has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Kaiyan Zhang, Biqing Qi, Bowen Zhou.

3 papers in our library · 2024–2025

OpenAlex · h-index 5 · 24 works · 68 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 154 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6959

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

🎓Tsinghua UniversityBeijing · China2024–2026 · 14 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2025–2026 · 5 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 9The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 2 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksBiochemistry, Genetics and Molecular Biology · 2 worksHealth Informatics · 2 worksMedicine · 2 worksMolecular Biology · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TTRL: Test-Time Reinforcement Learning (2025 · 76 citations)
    Reinforcement learning is now a main way to strengthen reasoning in large language models, but it usually needs labeled answers.
  2. MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding (2025 · 65 citations)
    Popular medical QA benchmarks such as MedQA and MMLU (Medical) no longer challenge frontier models. o1-preview reportedly reaches 96% on MedQA-USMLE. Multimodal sets like VQA-Rad or PMC-VQA mostly contain simple caption-derived questions without clinical context. MedXpertQA addresses both gaps. This multiple-choice…
  3. Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation (2024 · 19 citations)
    The paper asks whether large language models can propose genuinely new biomedical hypotheses, rather than repeating knowledge absorbed during pre-training.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. TTRL: Test-Time Reinforcement LearningConference on Neural Information Processing Systems (NeurIPS)Yuxin Zuo, Kaiyan Zhang, Li Sheng, et al.2025 · 76 citations
  2. MedXpertQA: Benchmarking Expert-Level Medical Reasoning and UnderstandingarXiv:2501.18362Yuxin Zuo, Shang Qu, Yifei Li, et al.2025 · 65 citations
  3. Large Language Models as Biomedical Hypothesis Generators: A Comprehensive EvaluationarXiv:2407.08940Biqing Qi, Kaiyan Zhang, Kai Tian, et al.2024 · 19 citations
  4. Automating Exploratory Proteomics Research via Language Models ↗arXiv:2411.03743 · no summary yetNing Ding et al.2024 · 6 citations

 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 154 times (of the 149,439 papers whose bibliographies we read); 55 citing papers with a page here →.

Updated

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