🧑‍🔬 Xiangchi Yuan

🏛️ Affiliations

🎓Georgia Institute of TechnologyAtlanta · United States2024–2026 · 8 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 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. Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models (2025 · 12 citations)
   Multimodal large language models can memorize private information, and erasing it is harder than in text-only LLMs.
2. Superficial Self-Improved Reasoners Benefit from Model Merging (2025 · 8 citations)
   Self-improvement, where a language model is fine-tuned on reasoning traces it generated itself, is often proposed as a remedy for scarce high-quality data.
3. Growing Through Experience: Scaling Episodic Grounding in Language Models (2025 · 5 citations)
   The paper addresses how language models can learn from past interaction experience (episodic grounding) to plan physical, multi-step tasks.
4. What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning (2026 · 4 citations)
   Curriculum learning, which sorts training examples from easy to hard, is often used to improve LLM reasoning.
5. Behavior Knowledge Merge in Reinforced Agentic Models (2026 · 3 citations)
   The paper tackles how to combine several agents, each specialized through reinforcement learning (RL), into one generalist model without further training.
6. MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues (2026 · 1 citation)
   Most safety benchmarks for multimodal large language models (MLLMs) test one image–prompt pair at a time. They miss harmful intent that builds up over a conversation or that is stated early and then hidden behind innocent follow-ups. MTMCS-Bench fills this gap with three-turn dialogues grounded in COCO-style images.…

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. Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, et al.2025 · 12 citations
2. Superficial Self-Improved Reasoners Benefit from Model MergingConference on Empirical Methods in Natural Language Processing (EMNLP)Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu, et al.2025 · 8 citations
3. Growing Through Experience: Scaling Episodic Grounding in Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Chunhui Zhang, Sirui Wang, Zhongyu Ouyang, et al.2025 · 5 citations
4. What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yaning Jia, Chunhui Zhang, Xingjian Diao, et al.2026 · 4 citations
5. Behavior Knowledge Merge in Reinforced Agentic ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiangchi Yuan, Dachuan Shi, Chunhui Zhang, et al.2026 · 3 citations
6. MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn DialoguesFindings of the Association for Computational LinguisticsZheyuan Liu, Dongwhi Kim, Yixin Wan, et al.2026 · 1 citation
7. Inference-Time Control for Trustworthy Large Language Models ↗Preprints.org · no summary yetYuyang Bai et al.2026

🤝 Co-authors here

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

• Chunhui Zhang ×5
• Soroush Vosoughi ×4
• Zheyuan Liu ×4
• Dachuan Shi ×2
• Meng Jiang ×2
• Wenke Lee ×2
• Zhaoxuan Tan ×2
• Zhongyu Ouyang ×2
• Chiyu Ma
• Fengran Mo
• Guangyao Dou
• Sirui Wang

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) ×4: ACL 2026 ×2, ACL 2025 ×2
• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Preprints.org

🔗 Cited by

Papers of our library cite their works 29 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated 31 October 2026

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