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Qichao Zhang

Qichao Zhang has 3 papers in our library, in 2026, most of them on Language models & pretraining; often with Dongbin Zhao, Jingbo Sun, Yaocheng Zhang.

3 papers in our library · 2026

OpenAlex · ORCID · h-index 1 · 6 works · 1 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 6 times in our library · ⭐ Paper Stars ranking: #10366

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

🔬Institute of AutomationBeijing · China2025–2026 · 3 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2025–2026 · 2 paperscurrent🎓University of Chinese Academy of SciencesBeijing · China2026 · 2 paperscurrent

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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksInformation Systems · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic (2026 · 6 citations)
    Search agents are LLMs that call a search engine across several reasoning turns. They are usually trained with reinforcement learning on a single outcome reward, so every query in a trajectory gets the same credit whether it helped or not. This sparse signal makes training slow and unstable, and it can cause reward…
  2. AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning (2026)
    Agentic RAG systems let LLMs call a retriever over several turns, but agents often keep searching after they already have enough evidence.
  3. Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026)
    The paper addresses LLM routing: picking, for each incoming query, a model from a pool that is good enough while keeping inference cost low.

 Co-authors here

10 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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