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Jingwei Ni

Jingwei Ni has 3 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning, Interpretability; often with Ekaterina Fadeeva, Mrinmaya Sachan, Tianyi Wu.

3 papers in our library · 2026

OpenAlex · h-index 8 · 28 works · 318 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 114 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #12757

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

🎓ETH ZurichZurich · Switzerland2022–2026 · 53 paperscurrent🎓University of ZurichZurich · Switzerland2023–2026 · 16 papers🎓University College LondonLondon · United Kingdom2021–2022 · 8 papers

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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksEducation · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksInterpretability · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models (2026 · 1 citation)
    Test-time scaling improves LLM reasoning by sampling several candidate reasoning steps or full chains and keeping the most promising ones.
  2. Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation (2026)
    Automatic fallacy detectors can flag flawed arguments, but they do not teach people to spot such reasoning on their own.
  3. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026)
    Research on test-time compute (TTC) scaling for LLM reasoning is scattered. Methods are tested under different protocols, model setups and budgets. Most papers report accuracy but not cost, and released code is rarely ready for production. ThinkBooster addresses this with a single open-source framework (MIT license)…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. LEXam: Benchmarking Legal Reasoning on 340 Law Exams ↗arXiv:2505.12864 · no summary yetYu Fan et al.2025 · 50 citations
  2. Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills ↗arXiv:2603.25158 · no summary yetJingwei Ni et al.2026 · 12 citations
  3. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.2026 · 1 citation
  4. Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical ArgumentationAnnual Meeting of the Association for Computational Linguistics (ACL)Minjing Shi, Junling Wang, Jingwei Ni, et al.2026
  5. ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.2026
  6. Last Translation Benchmark ↗arXiv:2609.04173 · no summary yetVilem Zouhar et al.2026

 Co-authors here

12 of 13 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 114 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

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