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Ming Hu

Ming Hu has 2 papers in our library, in 2026, most of them on Reasoning, Information retrieval & search, Efficient models & compression; often with Cheng Wang, Feilong Tang, Imran Razzak.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 16 · 69 works · 914 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13055

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

🎓Monash UniversityMelbourne · Australia2025–2026 · 2 paperscurrent🎓National Taiwan UniversityTaipei · Taiwan2010–2025 · 25 papers🎓Johns Hopkins UniversityBaltimore · United States2005–2008 · 4 papers🎓Sichuan UniversityChengdu · China2025 · 3 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2009–2010 · 3 papers🎓University of TorontoToronto · Canada2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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).Arts and Humanities · 2 worksComputer Science · 2 worksMedicine · 1 workArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workHealth Informatics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workReasoning · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification (2026 · 1 citation)
    The paper targets a weakness of LLMs in medical question answering. Prompting tricks such as Chain-of-Thought tend to give shallow or unstable reasoning, and fine-tuned medical models struggle when the data distribution shifts. The authors propose TAGS, which runs purely at inference time and changes no model…
  2. SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026)
    The paper targets two weaknesses of speculative decoding (SD) for LLMs. Classic "draft-then-verify" pipelines run strictly in sequence, which caps the speedup. Fully parallel schemes such as PEARL keep the large target model busy almost constantly, which raises the cost of using it. SPIDE is a training-free framework…

 Co-authors here

7 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

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ming-hu/