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Minzheng Wang

Minzheng Wang has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Question answering, Evaluation & benchmarks; often with Haiyang Yu, Nan Xu, Run Luo.

3 papers in our library · 2024–2026

OpenAlex · h-index 4 · 23 works · 60 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 62 times in our library · ⭐ Paper Stars ranking: #7197

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

🔬Institute of AutomationBeijing · China2023–2026 · 19 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2023–2026 · 17 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2023–2026 · 12 paperscurrent🏢Alibaba GroupHangzhou · China2024–2025 · 2 papers

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 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 · 2 works

🧭 Topics 3Research subjects and languages the work is about.Question answering · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024 · 72 citations)
    Most current long-context benchmarks make inputs longer by padding them with unrelated filler text. In those setups, the evidence for an answer usually sits in one place, so a model can skip most of the input and still answer correctly. Wang et al. address this with Loong, a multi-document QA benchmark where every…
  2. EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (2025 · 5 citations)
    Most instruction-following benchmarks test either one task with several constraints or several tasks with one constraint each.
  3. Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026)
    The paper looks at what goes wrong when self-play reinforcement learning with verifiable rewards (RLVR) moves from closed tasks like math or code to open-ended social language games.

 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 62 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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