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Junzhe Wang

Junzhe Wang has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Ming Zhang, Zhiheng Xi, Bichen Wang.

4 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 8 · 27 works · 1,049 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 427 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #2083

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. 4 more institutions on single bylines are not listed.

 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. The Rise and Potential of Large Language Model Based Agents: A Survey (2023 · 1,272 citations)
    This 2023 survey from the Fudan NLP Group maps the young field of agents built on large language models. The authors argue that earlier agent research mostly tuned algorithms for narrow skills, such as symbolic reasoning or board games like Go and Chess. What the field lacked was a general base model that agents…
  2. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026 · 3 citations)
    Fixed public test sets can leak into training corpora and be overfitted, so leaderboard scores may overstate real LLM ability.
  3. Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations (2026 · 2 citations)
    Most LLM-based counseling systems treat therapy as a single conversation. Real therapy such as Cognitive Behavioral Therapy (CBT) unfolds over many visits, with goals revised as the client changes. This paper frames automated counseling as a longitudinal task made of three parts: memory extraction, goal planning and…
  4. Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization (2026)
    Search agents built on LLMs learn to query external engines during multi-round reasoning, but reinforcement learning for such agents faces a dilemma.

All works

7 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning ↗arXiv:2402.05808 · no summary yetZhiheng Xi et al.2024 · 36 citations
  2. AgentGym: Evolving Large Language Model-based Agents across Diverse Environments ↗arXiv:2406.04151 · no summary yetZhiheng Xi et al.2024 · 33 citations
  3. AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZhiheng Xi et al.2026

 Co-authors here

12 of 20 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 427 times (of the 149,439 papers whose bibliographies we read); 93 citing papers with a page here →.

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