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Mingxu Chai

Mingxu Chai has 3 papers in our library, in 2026, most of them on Language models & pretraining, Information extraction; often with Guoqiang Zhang, Jiazheng Zhang, Zhiheng Xi.

3 papers in our library · 2026

OpenAlex · h-index 5 · 30 works · 38 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #12775

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

🎓Fudan UniversityShanghai · China2024–2026 · 16 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 1 work

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026 · 1 citation)
    Pre-trained text-and-layout models (PTLMs) score very highly on standard benchmarks for extracting information from visually-rich documents (VrD-IE), but they disappoint in practice.
  2. AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026)
    Verifiers, or reward models, steer test-time scaling (TTS) in LLM reasoning, but they often fail on hard mathematical problems.
  3. VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training (2026)
    Reward signals in LLM post-training are often noisy, sparse or ambiguous. When such errors pass through GAE, advantage estimates become distorted: credit drifts toward the end of a sequence (length hacking) or onto irrelevant tokens. Prior robust-RL work mostly cleans the reward model or filters data. This paper…

 Co-authors here

12 of 18 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); 1 citing paper with a page here →.

Updated

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