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Jialian Wu

Jialian Wu has 3 papers in our library, in 2025–2026, most of them on Reasoning; often with Jiang Liu, Xiaodong Yu, Ximeng Sun.

3 papers in our library · 2025–2026

OpenAlex · h-index 2 · 13 works · 83 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 110 times in our library · ⭐ Paper Stars ranking: #6315

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

🏢Advanced Micro DevicesSunnyvale · United States2026 · 4 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 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksDecision Sciences · 1 workInformation Systems and Management · 1 workInformation Systems · 1 work

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Lemma · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Agent Laboratory: Using LLM Agents as Research Assistants (2025 · 139 citations)
    Researchers can pursue only a fraction of their ideas because implementing experiments and writing them up take a lot of time and money.
  2. Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning (2026)
    Large language models can score well on math benchmarks yet still invoke lemmas whose hypotheses do not hold in the given context.
  3. Stabilizing Efficient Reasoning with Step-Level Advantage Selection (2026)
    Methods that make LLM reasoning shorter usually add length penalties or pruning. They also tend to post-train in a much smaller context window than the base model used. The authors separate these two factors. They post-train DeepScaleR-1.5B-Preview with plain GRPO in a 4K window, using only a correctness reward. This…

 Co-authors here

11 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 110 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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