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Xu Chu

Xu Chu has 5 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Yongxin Xu, Guanyu Wang, Junfeng Zhao.

5 papers in our library · 2025–2026

OpenAlex · h-index 1 · 7 works · 4 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 30 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7945

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

🎓Peking UniversityBeijing · China2025–2026 · 6 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).Computer Science · 5 worksArts and Humanities · 4 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 4 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksInformation retrieval & search · 1 workReasoning · 2 worksVision & language · 1 work

 Works by year

2020s

20262 papers20253 papers

 Papers

The most cited first (OpenAlex and our library).

  1. TC–RAG: Turing–Complete RAG's Case study on Medical LLM Systems (2025 · 25 citations)
    Retrieval-augmented generation can reduce hallucinations and stale knowledge in medical LLMs. However, existing RAG pipelines lack an explicit state variable that tells the system when to stop retrieving, so they can loop without converging or pile up noisy context. The authors propose TC–RAG, which they prove to be…
  2. Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025 · 11 citations)
    This paper tackles continual learning for large language models, where a model must absorb tasks one after another without losing earlier skills (catastrophic forgetting) while still benefiting from links between tasks (knowledge transfer).
  3. Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025 · 9 citations)
    Retrieval-augmented LLMs face two competing demands. They should trust retrieved evidence even when it contradicts what they memorized (adherence), and they should ignore retrieved passages that look relevant but are actually noise (robustness). Training aimed at one of these tends to hurt the other. The authors…
  4. MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026)
    Graph reasoning is usually handled by GNNs or graph transformers, which need task-specific training. LLMs that read graphs serialized as text struggle to keep two-dimensional structure and multi-hop relations. Vision-language methods such as GITA draw the entire graph as one picture, but large graphs then become…
  5. RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains (2026)
    Reinforcement learning for LLM reasoning usually rewards only the final answer. A model can therefore reach correct conclusions through faulty intermediate logic, which is a serious problem in finance, law and biomedicine. RADO exploits an asymmetry: finding one local flaw in a derivation is easier than producing a…

All works

6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. TC–RAG: Turing–Complete RAG's Case study on Medical LLM SystemsAnnual Meeting of the Association for Computational Linguistics (ACL)Xinke Jiang, Yue Fang, Rihong Qiu, et al.2025 · 25 citations
  2. Recurrent Knowledge Identification and Fusion for Language Model Continual LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yujie Feng, Xujia Wang, Zexin Lu, et al.2025 · 11 citations
  3. Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Yongxin Xu, Ruizhe Zhang, Xinke Jiang, et al.2025 · 9 citations
  4. MuSe: Multi-Stage Graph Reasoning via Vision-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Guanyu Wang, Xu Chu, Zhijie Tan, et al.2026
  5. RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes DomainsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijie Tan, Xu Chu, Guanyu Wang, et al.2026
  6. Robust Explanations for User Trust in Enterprise NLP Systems ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · no summary yetGuilin Zhang et al.2026

 Co-authors here

12 of 16 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 30 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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