---
title: "Zijun Yao · University of Kansas — author profile and papers"
description: "Zijun Yao, University of Kansas: 6 papers on Elamur Research (2024–2026), h-index 10 — affiliations, topics, co-authors and who cites them…"
canonical: "https://research.elamur.ai/free/en/authors/zijun-yao/"
---
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# Zijun Yao

Zijun Yao has 6 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Juanzi Li](https://md.research.elamur.ai/free/en/authors/juanzi-li/), [Lei Hou](https://md.research.elamur.ai/free/en/authors/lei-hou/), [Hao Peng](https://md.research.elamur.ai/free/en/authors/hao-peng/).

6 papers in our library · 2024–2026

[OpenAlex](https://openalex.org/A5046687207) · [ORCID](https://orcid.org/0000-0002-0288-9283) · [Wikidata](https://www.wikidata.org/wiki/Q130822908) · [**h-index 10**](https://openalex.org/authors/A5046687207) · [60 works](https://openalex.org/works?filter=author.id:A5046687207) · [324 citations](https://openalex.org/works?filter=author.id:A5046687207&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/zijun-yao/#papers) · [🔗 Cited 138 times in our library](https://md.research.elamur.ai/free/en/authors/zijun-yao/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #6343](https://md.research.elamur.ai/free/en/paper-stars/page/64/#star-6343)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Zijun+Yao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Zijun+Yao) · [dblp](https://dblp.org/search?q=Zijun+Yao) · [ACL Anthology](https://aclanthology.org/search/?q=Zijun+Yao)

## Affiliations

[🎓**Tsinghua University** Beijing · China 2021–2026 · 40 papers*current*](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) · [🔬**Beijing National Research Center for Information Science and Technology** Beijing · China 2021–2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/labs/beijing-national-research-center-for-information-science-and-technology/) · [🎓**University of Kansas** Lawrence · United States 2025–2026 · 5 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-kansas/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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.

📚 Shelves 12 The field of science this work belongs to (OpenAlex fields).[Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Health Informatics · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/health-informatics/) · [Health Information Management · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/health-information-management/) · [Health Professions · 1 work](https://md.research.elamur.ai/free/en/shelves/health-professions/) · [Medicine · 1 work](https://md.research.elamur.ai/free/en/shelves/medicine/)

🧭 Topics 3 Research subjects and languages the work is about.[Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/)

💡 Concepts 1 Key ideas and methods explained on our concept pages.[Inverted index · 1 work](https://md.research.elamur.ai/free/en/concepts/inverted-index/)

## Works by year

### 2020s

[**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/zijun-yao/year/2026/) · [**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/zijun-yao/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/zijun-yao/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)** *(2024 · 53 citations)* Reward models steer RLHF training and choose the best candidate output in inference-time scaling, yet existing benchmarks usually pit a strong model's answer against a weaker model's answer.
2. **[SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/yao-2025-seakr-self-aware-knowledge-retrieval/)** *(2025 · 52 citations)* Adaptive retrieval-augmented generation (RAG) aims to query a search engine only when a large language model actually lacks the knowledge it needs.
3. **[Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems](https://md.research.elamur.ai/free/en/papers/peng-2025-agentic-reward-modeling-integrating-human/)** *(2025 · 18 citations)* Reward models for LLMs are usually trained only on human preferences. They can favour superficial traits such as longer or more polished answers and miss factual mistakes or ignored constraints. The authors propose agentic reward modeling, in which the final reward is a weighted combination of a base preference score…
4. **[Pre-training Distillation for Large Language Models: A Design Space Exploration](https://md.research.elamur.ai/free/en/papers/peng-2025-pre-training-distillation-large-language/)** *(2025 · 14 citations)* Knowledge distillation for large language models is usually applied after pre-training, when a student is fine-tuned on responses written by a stronger teacher.
5. **[RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models](https://md.research.elamur.ai/free/en/papers/moghaddam-2026-reprompt-recurrent-prompt-tuning-integrating/)** *(2026)* When LLMs are applied to structured electronic health records, two weaknesses appear. Converting visit sequences into flat text blurs the timing between visits and the identity of individual codes. Each patient is also handled on its own, so the model gains nothing from patterns that recur across the cohort.
6. **[WildReward: Learning Reward Models from In-the-Wild Human Interactions](https://md.research.elamur.ai/free/en/papers/peng-2026-wildreward-learning-reward-models-wild/)** *(2026)* Reward models for LLM training usually depend on large sets of human-annotated preference pairs, which are expensive to collect.

## All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. [**RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style arXiv:2410.16184**Yantao Liu, Zijun Yao, Rui Min, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)
2. **[SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/yao-2025-seakr-self-aware-knowledge-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zijun Yao, Weijian Qi, Liangming Pan, et al.*2025 · 52 citations*
3. **[T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling ↗](https://arxiv.org/abs/2501.11651) arXiv:2501.11651 · no summary yet**Zhenyu Hou et al.*2025 · 28 citations*
4. **[Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems](https://md.research.elamur.ai/free/en/papers/peng-2025-agentic-reward-modeling-integrating-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Yunjia Qi, Xiaozhi Wang, et al.*2025 · 18 citations*
5. **[Pre-training Distillation for Large Language Models: A Design Space Exploration](https://md.research.elamur.ai/free/en/papers/peng-2025-pre-training-distillation-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Xin Lv, Yushi Bai, et al.*2025 · 14 citations*
6. **[RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models](https://md.research.elamur.ai/free/en/papers/moghaddam-2026-reprompt-recurrent-prompt-tuning-integrating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arya Hadizadeh Moghaddam, Drew Ross, Mohsen Nayebi Kerdabadi, et al.*2026*
7. **[WildReward: Learning Reward Models from In-the-Wild Human Interactions](https://md.research.elamur.ai/free/en/papers/peng-2026-wildreward-learning-reward-models-wild/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Yunjia Qi, Xiaozhi Wang, et al.*2026*

## Co-authors here

12 of 14 co-authors with a page, the most papers together first.

- [Juanzi Li](https://md.research.elamur.ai/free/en/authors/juanzi-li/) ×5
- [Lei Hou](https://md.research.elamur.ai/free/en/authors/lei-hou/) ×5
- [Hao Peng](https://md.research.elamur.ai/free/en/authors/hao-peng/) ×3
- [Xiaozhi Wang](https://md.research.elamur.ai/free/en/authors/xiaozhi-wang/) ×2
- [Yunjia Qi](https://md.research.elamur.ai/free/en/authors/yunjia-qi/) ×2
- [Bin Xu](https://md.research.elamur.ai/free/en/authors/bin-xu/)
- [Dongjie Wang](https://md.research.elamur.ai/free/en/authors/dongjie-wang/)
- [Jiajie Zhang](https://md.research.elamur.ai/free/en/authors/jiajie-zhang/)
- [Liangming Pan](https://md.research.elamur.ai/free/en/authors/liangming-pan/)
- [Shulin Cao](https://md.research.elamur.ai/free/en/authors/shulin-cao/)
- [Xin Lv](https://md.research.elamur.ai/free/en/authors/xin-lv/)
- [Yantao Liu](https://md.research.elamur.ai/free/en/authors/yantao-liu/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×4: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ×3
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)

## Cited by

Papers of our library cite their works 138 times (of the 149,439 papers whose bibliographies we read); [41 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/zijun-yao/cited-by/).

Updated 1 November 2026