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Zhichao Xu

Zhichao Xu has 5 papers in our library, in 2025–2026, most of them on Language models & pretraining, Information retrieval & search; often with Kiran Ramnath, Lin Lee Cheong, Yun Zhou.

5 papers in our library · 2025–2026

OpenAlex · h-index 1 · 2 works · 1 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 35 times in our library · ⭐ Paper Stars ranking: #8435

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 UtahSalt Lake City · United States2026 · 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 9The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArts and Humanities · 3 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksDecision Sciences · 1 workInformation Systems and Management · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20264 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025 · 65 citations)
    Retrieval-augmented generation can be undermined by retrieved passages that are off-topic or misleading. Such passages lower answer quality and add inference cost. MAIN-RAG is a post-retrieval filtering framework that needs no training or fine-tuning and runs on top of a standard retriever. It uses three LLM agents:
  2. Reinforcement Learning for Self-Improving Agent with Skill Library (2026 · 8 citations)
    Agents built on LLMs and trained with RL often stay tied to their training scenarios. They struggle to turn experience gathered after deployment into better performance on later tasks. Skill libraries, where agents store reusable routines, can help, but current versions rely on hand-written prompts and so depend on…
  3. Learning to Ideate for Machine Learning Engineering Agents (2026 · 2 citations)
    Agents for machine learning engineering (MLE) often stop once they have one working solution and do not keep refining it.
  4. BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation (2026)
    This paper addresses automatic agentic workflow generation: composing LLM calls, tool use and post-processing into executable pipelines without manual design.
  5. Teaching People LLM's Errors and Getting it Right (2026)
    Users often trust LLMs on tasks the models handle poorly. An earlier approach, the AI-integration teaching pipeline of Mozannar et al. (2023), tried to fix this. It finds regions where a model tends to fail, describes them in natural language and teaches users to avoid the model there. However, it gave no gains for…

 Co-authors here

12 of 22 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 35 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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