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Weizhi Zhang

Weizhi Zhang has 5 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Henry Peng Zou, Yankai Chen, Hanrong Zhang.

5 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 4 · 12 works · 175 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 113 times in our library · ⭐ Paper Stars ranking: #6404

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

🎓University of Illinois ChicagoChicago · United States2023–2025 · 5 paperscurrent🎓University of VirginiaCharlottesville · United States2025 · 2 paperscurrent🎓University of WashingtonSeattle · United States2025 · 1 papercurrent

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).Computer Science · 5 worksArtificial Intelligence · 5 worksArts and Humanities · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20262 papers20253 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Model Agent: A Survey on Methodology, Applications and Challenges (2025 · 62 citations)
    Work on LLM-based agents has grown quickly but remains scattered. Existing reviews tend to cover one slice, such as games, deployment settings, multimodality or security, or they give broad overviews without a method-level breakdown. Luo et al. address this with a survey built around how agents are engineered. Its…
  2. WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025 · 36 citations)
    Reinforcement learning has improved LLMs mostly on single-turn problems like math. Web agents are harder to train this way: they must make long sequences of decisions on changing pages, and earlier RL approaches for this setting depended on offline or off-policy pipelines with extra components. WebAgent-R1 is a…
  3. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs (2025 · 17 citations)
    Large language models face two linked weaknesses. Their stored knowledge is fixed and can be wrong, and they struggle with problems that need several reasoning steps. Retrieval-Augmented Generation (RAG) addresses the first, and reasoning techniques such as Chain-of-Thought address the second. This survey argues that…
  4. LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026 · 12 citations)
    Fully autonomous LLM agents remain hard to trust in practice. They hallucinate, especially across chained actions. They also falter on long, expertise-heavy tasks and raise safety, bias and accountability concerns in areas such as finance or healthcare. This survey argues for keeping people in the loop and gives what…
  5. GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026 · 5 citations)
    LLM agents in long conversations must absorb new information without letting passing chatter corrupt what they already know.

 Co-authors here

12 of 32 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 113 times (of the 149,439 papers whose bibliographies we read); 38 citing papers with a page here →.

Updated

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