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MingHua Ma

MingHua Ma has 4 papers in our library, in 2025–2026, most of them on Reasoning; often with Bing Qin, Bingbing Xu, Bo Qiao.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 24 · 77 works · 1,962 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 30 times in our library · ⭐ Paper Stars ranking: #9369

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

🏢MicrosoftRedmond · United States2023–2026 · 34 paperscurrent🏢Microsoft Research AsiaBeijing · China2022–2024 · 14 papers🎓Tsinghua UniversityBeijing · China2016–2025 · 10 papers🔬Beijing National Research Center for Information Science and TechnologyBeijing · China2018–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 3 worksHuman-Computer Interaction · 1 workComputer Networks and Communications · 1 workDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 2 works

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. UFO: A UI-Focused Agent for Windows OS Interaction (2025 · 38 citations)
    Windows is a hard environment for vision-language agents. Its interfaces are dense, its screens are large, and everyday requests often require moving between several programs. The Microsoft authors present UFO, an agent that turns natural-language instructions into executed GUI actions on Windows. It relies on two…
  2. Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization (2026)
    SFT on distilled chain-of-thought data usually relies on rejection sampling, which keeps only trajectories whose final answer is correct.
  3. PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning (2026)
    Large Reasoning Models handle complex problems well but often ignore explicit constraints in user instructions.
  4. TabGuard: Agentic LLM Orchestration for Adaptive Tabular Anomaly Detection via Dynamic Validator Selection and Generation (2026)
    TabGuard tackles column-level anomaly detection in heterogeneous tables, which mix identifiers, numbers, and free text. Rule-based checkers need manual setup for every schema. Statistical detectors miss semantic errors. Running an LLM on every cell costs too much at enterprise scale.

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

Updated

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