🧑‍🔬 Tong Ruan

🏛️ Affiliations

🎓East China University of Science and TechnologyShanghai · China2026 · 13 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Can Multimodal Large Language Models Understand Spatial Relations? (2025 · 11 citations)
   Spatial relation reasoning is a basic skill for multimodal large language models (MLLMs), but existing benchmarks have several weaknesses.
2. AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online Hospital (2026)
   Clinical assistants built on LLMs, whether prompted directly or grounded through text or graph retrieval (RAG), mostly receive isolated medical facts.
3. MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property Prediction (2026)
   Large language models could make molecular property prediction (MPP) explainable. However, they have trouble connecting molecular structure to activity. Earlier methods that supply structural hints also tend to treat chance co-occurrences in training data as chemical laws. MCLE-Mol addresses this with a framework…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Can Multimodal Large Language Models Understand Spatial Relations?Annual Meeting of the Association for Computational Linguistics (ACL)Jingping Liu, Ziyan Liu, Zhedong Cen, et al.2025 · 11 citations
2. AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online HospitalAnnual Meeting of the Association for Computational Linguistics (ACL)Lianxin Sun, Xiaoying Ying, Guangya Yu, et al.2026
3. MCLE-Mol: Empowering LLM with Molecular Comprehension and Low-Cost Continual Evolution for Interpretable Property PredictionFindings of the Association for Computational LinguisticsZhili Pu, Lantian Zhang, Hao Duan, et al.2026
4. MedKInstruct: A Multimodal Knowledge Graph Based Framework for Multi-Hop and Hard-Negative Instruction Data Synthesis in MedVQA ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYinan Wu et al.2026
5. Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetGuangya Yu et al.2026
6. Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetMu Zhang et al.2026

🤝 Co-authors here

8 co-authors with a page, the most papers together first.

• Weiyan Zhang ×2
• Guangya Yu
• Haiyun Jiang
• Hao He
• Jingping Liu
• Yan Zhou
• Yun Tang
• Ziyan Liu

Venues

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

• Findings of the Association for Computational Linguistics: ACL 2026 ×3
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026, ACL 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 6 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 1 November 2026

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