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Qizhou Chen

Qizhou Chen has 4 papers in our library, in 2025–2026, most of them on Question answering, Language models & pretraining, Reasoning; often with Chengyu Wang, Taolin Zhang, Xiaofeng He.

4 papers in our library · 2025–2026

OpenAlex · h-index 3 · 15 works · 15 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 6 times in our library · ⭐ Paper Stars ranking: #11238

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

🎓East China Normal UniversityShanghai · China2024–2026 · 16 paperscurrent🏢Alibaba GroupHangzhou · China2024–2025 · 4 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Question answering · 2 worksReasoning · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering (2025 · 9 citations)
    Multi-hop question answering requires collecting several pieces of evidence and chaining them together, but existing LLM-based approaches usually apply one fixed strategy to every question.
  2. AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026)
    LLMs answering knowledge-intensive questions often hallucinate and struggle with long-tail facts. Existing multi-agent QA systems are trained in one of three ways: module by module, end to end, or in a global-local scheme. None of these adapts which agents run for a given question or models how agents depend on each…
  3. QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026)
    Large language models still produce factual errors. Model editing can patch individual facts, but in sequential model editing (SME), where corrections arrive one after another, earlier edits may conflict with new ones and the model's general abilities can degrade. The authors propose QueueEDIT, which combines two…
  4. Taming “Zombie” Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution (2026)
    LLM-based multi-agent systems often save compute by pruning agents or links from the communication graph. These cuts are permanent. An agent that fails for a moment, through a hallucination or a knowledge gap, is removed even if it could have helped in later rounds. The authors call such agents "zombie" agents. Their…

 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.

 Cited by

Papers of our library cite their works 6 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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