🧑‍🔬 Who cites Jiayi Geng

70 citations of their works from 69 papers of our library (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · ← Jiayi Geng

1. How to Train Long-Context Language Models (Effectively)Annual Meeting of the Association for Computational Linguistics (ACL)Tianyu Gao, Alexander Wettig, Howard Yen, et al.2025 · 89 citations
2. Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI TutorsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kaushal Kumar Maurya, Kv Aditya Srivatsa, Kseniia Petukhova, et al.2025 · 53 citations
3. Towards responsible development of generative AI for education: An evaluation-driven approachJurenka2024 · 53 citations
4. MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model SeriesarXiv:2405.19327Ge Zhang, Scott Qu, Jiaheng Liu, et al.2024 · 44 citations
5. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv:2507.21046Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.2025 · 36 citations
6. Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model TutorsConference on Empirical Methods in Natural Language Processing (EMNLP)Nico Daheim, Jakub Macina, Manu Kapur, et al.2024 · 35 citations
7. MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM TutorsConference on Empirical Methods in Natural Language Processing (EMNLP)Jakub Macina, Nico Daheim, Ido Hakimi, et al.2025 · 33 citations
8. CiteME: Can Language Models Accurately Cite Scientific Claims?Conference on Neural Information Processing Systems (NeurIPS)Ori Press, Andreas Hochlehnert, Ameya Prabhu, et al.2024 · 25 citations
9. From AI for Science to Agentic Science: A Survey on Autonomous Scientific DiscoveryarXiv:2508.14111Jiaqi Wei, Yuejin Yang, Xiang Zhang, et al.2025 · 19 citations
10. From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)David Dinucu-Jianu, Jakub Macina, Nico Daheim, et al.2025 · 19 citations
11. EXP-Bench: Can AI Conduct AI Research Experiments?arXiv:2505.24785Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu, et al.2025 · 17 citations
12. Can Large Reasoning Models Self-Train?arXiv:2505.21444Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.2025 · 16 citations
13. The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical ProofsarXiv:2506.21621Jasper Dekoninck, Ivo Petrov, Kristian Minchev, et al.2025 · 11 citations
14. EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific DiscoveryarXiv:2603.08127Yougang Lyu, Xi Zhang, Xinhao Yi, et al.2026 · 10 citations
15. CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended DiscoveryarXiv:2604.01658Ao Qu, Han Zheng, Zijian Zhou, et al.2026 · 9 citations
16. Mem²Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Zihao Cheng, Zeming Liu, Yingyu Shan, et al.2026 · 5 citations
17. Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge InjectionAnnual Meeting of the Association for Computational Linguistics (ACL)Yuwei Zhang, Wenhao Yu, Shangbin Feng, et al.2026 · 4 citations
18. MemoBrain: Executive Memory as an Agentic Brain for ReasoningFindings of the Association for Computational LinguisticsHongjin Qian, Zhao Cao, Zheng Liu2026 · 1 citation
19. On Safety Risks in Experience-Driven Self-Evolving AgentsFindings of the Association for Computational LinguisticsWeixiang Zhao, Yichen Zhang, Yingshuo Wang, et al.2026 · 1 citation
20. What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary SearchFindings of the Association for Computational LinguisticsXinhao Zhang, Xi Chen, François Portet, et al.2026 · 1 citation
21. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.2026
22. Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual LearningFindings of the Association for Computational LinguisticsZheng Wu, Xingyu Lou, Xinbei Ma, et al.2026
23. GUI0: Self-Evolving Foundational GUI Agents in Super App EcosystemsAnnual Meeting of the Association for Computational Linguistics (ACL)Xinyi Wang, Wei Dai, Kyle Qiao, et al.2026
24. Self-Sum: Teaching an Agent to Decide Itself When and What to SummarizeFindings of the Association for Computational LinguisticsHongru Wang, Rui Wang, Jushi Kai, et al.2026
25. Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience RefinementFindings of the Association for Computational LinguisticsCheng Yang, Xuemeng Yang, Licheng Wen, et al.2026

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