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Yu He

Yu He has 3 papers in our library, in 2026, most of them on Language models & pretraining; often with Baolei Zhang, Biao Yi, Dacheng Tao.

3 papers in our library · 2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · ⭐ Paper Stars ranking: #13391

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

🏢Huawei TechnologiesShenzhen · China2026 · 2 paperscurrent🎓Northwestern Polytechnical UniversityXi'an · China2026 · 2 paperscurrent🎓Eastern Institute of Technology, NingboNingbo · China2026 · 1 papercurrent🎓Zhejiang UniversityHangzhou · China2026 · 1 papercurrent

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 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 1 workDecision Sciences · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 workManagement Science and Operations Research · 1 work

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MAXS: Meta-Adaptive Exploration with LLM Agents (2026)
    The paper addresses inference-time decision making in tool-using LLM agents, where step-by-step methods such as CoT and ToT decide greedily without looking ahead, and small early mistakes can push a multi-tool trajectory off course.
  2. Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026)
    The paper addresses automated price quoting for a large tourism operator. Incoming travel orders are messy, mostly chat screenshots, and pricing rules sit in policy documents that change constantly. Hand-coded rule engines need developer work for every new clause, while letting an LLM compute prices freely is too…
  3. SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026)
    Membership inference attacks (MIAs) test whether a given text was part of a model's training data, which matters for privacy audits, copyright checks and verifying machine unlearning.

 Co-authors here

12 of 19 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.

Updated

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