🧑‍🔬 Yong Li

🏛️ Affiliations

🏢Ant GroupHangzhou · China2026 · 2 paperscurrent🎓Tsinghua UniversityBeijing · China2026 · 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities (2024 · 86 citations)
   Agent-based models (ABM) of the economy usually drive households with hand-written rules or trained neural networks.
2. EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education (2023 · 58 citations)
   General-purpose LLMs are poorly suited to education. They lack subject and pedagogical knowledge, their knowledge goes stale, and they tend to hallucinate. EduChat is an open-source chatbot built to serve teachers, students and parents. The authors draw on educational and psychological theory and train it in two…
3. CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026 · 1 citation)
   Vision-language models increasingly act as the reasoning core of embodied agents such as drones, robots and self-driving cars.
4. Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning (2026 · 1 citation)
   Multi-step reasoning methods such as Chain-of-Thought and Tree-of-Thought are expensive because autoregressive LLMs must decode every candidate step one token at a time.
5. EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026)
   Agentic test-time scaling (TTS) helps LLM agents resolve repository-level software issues, but running many trajectories is costly, and choosing the best candidate patch is unreliable.
6. RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning Models (2026)
   Large reasoning models (LRMs) can produce fluent chains of thought that still hide small arithmetic slips or broken constraints, which then lead to wrong final answers.
7. Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026)
   Multimodal LLM agents that operate phone and desktop interfaces work best at large scale, but such models are too costly to run on end-user devices.

All works

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

1. EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic ActivitiesAnnual Meeting of the Association for Computational Linguistics (ACL)Nian Li, Chen Gao, Mingyu Li, et al.2024 · 86 citations
2. EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent EducationarXiv:2308.02773Yuhao Dan, Zhikai Lei, Yiyang Gu, et al.2023 · 58 citations
3. CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban EnvironmentsAnnual Meeting of the Association for Computational Linguistics (ACL)Haotian Xu, Yue Hu, Zhengqiu Zhu, et al.2026 · 1 citation
4. Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Chenyang Shao, Sijian Ren, Fengli Xu, et al.2026 · 1 citation
5. EGSS: Entropy-guided Stepwise Scaling for Reliable Software EngineeringAnnual Meeting of the Association for Computational Linguistics (ACL)Chenhui Mao, Yuanting Lei, Zhixiang Wei, et al.2026
6. RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zihang Liu, Fang Zhouhua, Hui Liu, et al.2026
7. Towards Scalable Lightweight GUI Agents via Multi-role OrchestrationFindings of the Association for Computational LinguisticsZiwei Wang, Junjie Zheng, Leyang Yang, et al.2026
8. FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetKe Xu et al.2026
9. Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhilun Zhou et al.2026

🤝 Co-authors here

12 of 21 co-authors with a page, the most papers together first.

• Chen Gao ×2
• Dajun Chen ×2
• Fang Zhouhua ×2
• Zhiwei Liu ×2
• Aimin Zhou
• Chenyang Shao
• Fengli Xu
• Haotian Xu
• Hui Liu
• Jiaju Lin
• Jiajun Bu
• Quanjun Yin

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) ×5: ACL 2026 ×4, ACL 2024
• Findings of the Association for Computational Linguistics: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 59 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated 1 November 2026

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