Ruoxi Sun Email: ruoxis at google dot com   I am a senior research scientist at Google Cloud AI Research. My research interests are large language models for code generation, agent, and factuality. I obtained my Ph.D. degree in Computational Neuroscience from Columbia University in 2019.   Publications [Google Scholar] | Education | Misc.

Publications

• Learn-by-interact: Synthesize Large-scale Agent Data with Trajectories by Interacting with Environments

  Hongjin Su, Ruoxi Sun, Jinsung Yoon, Pengcheng Yin, Sercan O Arik*, Tao Yu*. (* Equal contribution)

  International Conference on Learning Representations (ICLR under-review), 2025.




• Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

  Fangyu Lei, Jixuan Chen, Yuxiao Ye, Ruisheng Cao, Dongchan Shin, Hongjin Su, Zhaoqing Suo, Hongcheng Gao, Wenjing Hu, Pengcheng Yin, Victor Zhong, caiming xiong, Ruoxi Sun, Qian Liu, Sida Wang, Tao Yu.

  International Conference on Learning Representations (ICLR under-review), 2025.




• Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models

  Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, Sercan O Arik.

  International Conference on Learning Representations (ICLR under-review), 2025.




• From Few to Many: Enhancing Many-Shot In-Context Learning with Optimized Example Selection and Expansion

  Xingchen Wan, Han Zhou, Ruoxi Sun, Sercan O Arik.

  International Conference on Learning Representations (ICLR under-review), 2025.




• CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL.

  Mohammadreza Pourreza*, Hailong Li*, Ruoxi Sun, Yeounoh Chung, Shayan Talaei, Gaurav Tarlok Kakkar, Yu Gan, Amin Saberi, Fatma Ozcan, Sercan O Arik. (* Equal contribution)

  International Conference on Learning Representations (ICLR under-review), 2025.




• SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging

  Mohammadreza Pourreza, Ruoxi Sun, Hailong Li, Lesly Miculicich, Tomas Pfister, Sercan O. Arik.

  International Conference on Learning Representations (ICLR under-review), 2025.




• Learning to Clarify: Multi-turn Conversations with Action- Based Contrastive Self-Training

  Maximillian Chen, Ruoxi Sun, Sercan Ö. Arık, Tomas Pfister.

  International Conference on Learning Representations (ICLR under-review), 2025.




• BRIGHT: a realistic Benchmark for ReasonInG-Heavy reTrieval

  Hongjin Su, Howard Yen, Mengzhou Xia, Han-yu Wang, Haisu Liu, Niklas Muennighoff, Weijia Shi, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik, Danqi Chen, Tao Yu.

  International Conference on Learning Representations (ICLR under-review), 2025.




• Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization

  Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan O. Arik.

  Conference on Neural Information Processing Systems (NeurIPS), 2024.




• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

  Ruisheng Cao, Fangyu Lei, Haoyuan Wu, Jixuan Chen, Yeqiao Fu, Hongcheng Gao, Xinzhuang Xiong, Hanchong Zhang, Yuchen Mao, Wenjing Hu, Tianbao Xie, Hongshen Xu, Danyang Zhang, Sida Wang, Ruoxi Sun, Pengcheng Yin, Caiming Xiong, Ansong Ni, Qian Liu, Victor Zhong, Lu Chen, Kai Yu, Tao Yu.

  Conference on Neural Information Processing Systems (NeurIPS), 2024.




• Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

  Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, Sercan Ö. Arik.

  Conference on Neural Information Processing Systems (NeurIPS), 2024.




• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL

  Ruoxi Sun, Sercan O. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister.

  Transactions on Machine Learning Research (TMLR under-review), 2024.




• Capabilities of Gemini Models in Medicine

  Khaled Saab, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, Tim Strother, Chunjong Park, Elahe Vedadi, Juanma Zambrano Chaves, Szu-Yeu Hu, Mike Schaekermann, Aishwarya Kamath, Yong Cheng, David G.T. Barrett, Cathy Cheung, Basil Mustafa, Anil Palepu, Daniel McDuff, Le Hou, Tomer Golany, Luyang Liu, Jean-baptiste Alayrac, Neil Houlsby, Nenad Tomasev, Jan Freyberg, Charles Lau, Jonas Kemp, Jeremy Lai, Shekoofeh Azizi, Kimberly Kanada, SiWai Man, Kavita Kulkarni, Ruoxi Sun, Siamak Shakeri, Luheng He, Ben Caine, Albert Webson, Natasha Latysheva, Melvin Johnson, Philip Mansfield, Jian Lu, Ehud Rivlin, Jesper Anderson, Bradley Green, Renee Wong, Jonathan Krause, Jonathon Shlens, Ewa Dominowska, S. M. Ali Eslami, Katherine Chou, Claire Cui, Oriol Vinyals, Koray Kavukcuoglu, James Manyika, Jeff Dean, Demis Hassabis, Yossi Matias, Dale Webster, Joelle Barral, Greg Corrado, Christopher Semturs, S. Sara Mahdavi, Juraj Gottweis, Alan Karthikesalingam, Vivek Natarajan.

  Nature under-review (Journal), 2024.




• Effective Large Language Model Adaptation for Improved Grounding

  Xi Ye, Ruoxi Sun, Sercan O. Arik, Tomas Pfister.

  The North American Chapter of the Association for Computational Linguistics (NAACL), 2024.




• Universal Self-adaptive Prompting

  Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan O Arik, Tomas Pfister.

  Empirical Methods in Natural Language Processing (EMNLP), 2023.




• SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data

  Ruoxi Sun, Sercan O Arik, Rajarishi Sinha, Hootan Nakhost, Hanjun Dai, Pengcheng Yin, Tomas Pfister.

  Findings of Empirical Methods in Natural Language Processing (EMNLP Findings), 2023.




• Better Zero-Shot Reasoning with Self-Adaptive Prompting

  Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Arik, Tomas Pfister.

  Findings of Association for Computational Linguistics (ACL Findings), 2023.




• Learning to Prompt for Continual Learning

  Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister.

  Conference on Computer Vision and Pattern Recognition (CVPR), 2022.




• DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning

  Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister.

  European Conference on Computer Vision (ECCV), 2022.




• Does GNN Pretraining Help Molecular Representation?

  Ruoxi Sun, Hanjun Dai, Adams Yu.

  Conference on Neural Information Processing Systems (NeurIPS), 2022.




• Neural Spline Search for Quantile Probabilistic Forecasting

  Ruoxi Sun*, Chun-Liang Li*, Sercan Arik, Chen-Yu Lee, Mike Dusenberry, Tomas Pfister. (* Equal contribution)

  Association for the Advancement of Artificial Intelligence (AAAI), 2022.




• Reverse engineering learned optimizers reveals known and novel mechanisms

  Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein.

  Conference on Neural Information Processing Systems (NeurIPS), 2021.




• Towards understanding retrosynthesis by energy-based models

  Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai.

  Conference on Neural Information Processing Systems (NeurIPS), 2021.




• Kohn-Sham equations as regularizer: Building prior knowledge into machine-learned physics

  Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D Cubuk, Patrick Riley, Kieron Burke.

  Physical Review Letters (Journal), 2021.




• Using a thousand optimization tasks to learn hyperparameter search strategies

  Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein.

  Preprint, 2021.




• NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging in C. elegans

  Eviatar Yemini, Albert Lin, Amin Nejatbakhsh, Erdem Varol, Ruoxi Sun, Gonzalo E Mena, Aravinthan DT Samuel, Liam Paninski, Vivek Venkatachalam, Oliver Hobert.

  Cell (Journal), 2021.




• Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models

  Ruoxi Sun, Scott Linderman, Liam Paninski.

  Conference on Neural Information Processing Systems (NeurIPS), 2019. (Oral)




• Scalable approximate Bayesian inference for particle tracking data

  Ruoxi Sun, Liam Paninski.

  International Conference on Machine Learning (ICML), 2018.




• Scalable variational inference for super resolution microscopy

  Ruoxi Sun, Evan Archer, Liam Paninski.

  International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

Education

• Aug 2013 - May 2019: Ph.D., Computational Neuroscience, Columbia University. Advisor: Prof. Liam Paninski.

Miscellaneous

I love reading WUXIA novels. I also did biological research during my undergraduate