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Weiqin Wang

Weiqin Wang has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Hui Huang, Yile Wang, Sijie Cheng.

2 papers in our library · 2026

OpenAlex · h-index 2 · 9 works · 12 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10776

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning (2026 · 4 citations)
    Test-time reinforcement learning (TTRL) trains LLMs on reasoning tasks without gold answers. It samples many responses and treats the majority-voted answer as a pseudo-label. This has two weaknesses. Every vote counts equally, so a popular but wrong answer gets reinforced (confirmation bias), and the binary match…
  2. AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models (2026)
    LLM memory systems such as A-Mem and Mem0 repeatedly summarize and rewrite past interactions. This can wash out fine details. Entity-graph indexing, as in HippoRAG 2, can instead link unrelated memories through common, generic entities. AnchorMem addresses both problems by separating what is used for retrieval from…

 Co-authors here

4 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 4 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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