about

Ruocheng Guo is a Staff Research Scientist at Intuit AI Research. His current research focus is LLM Agents, he worked on LLM reasoning, causal machine learning, and conformal prediction. Prior to joining Intuit, he was a Researcher at TikTok/ByteDance Research and an Assistant Professor at City University of Hong Kong. He earned his Ph.D. from the Data Mining and Machine Learning Laboratory (DMML) at Arizona State University, advised by Huan Liu.

Research Interests:

• LLMs (Agents, Dialogue, and Tool Use)
• Causal ML
• Recommendation Systems
• Data Mining

News

• 05/25 Got 2 papers in IJCAI’25, 1 paper in ACL’25, and 1 paper in KDD’25
• 04/25 Gave a talk at LinkedIn Market AI on LLM Agents slides, thank Dr. Liangjie Hong for the invitation
• 03/25 Joined Intuit AI Research as a Staff RS
• 11/24 Invited to serve as PC co-chair of the industry track of IEEE DSAA’25
• 11/24 Invited by Dr. Jing Ma to give a guest lecture on conformal causal inference at Case Western Reserve University slides
• 10/24 A paper accepted to NeurIPS’24
• 08/24 Invited to present at 2nd Workshop on Causal Inference and Machine Learning in Practice @ KDD’24

Recent Papers

Recent Preprints

• Function Calling in Large Language Models: Industrial Practices, Challenges, and Future Directions [arxiv]

Recent Publications

• Stepwise Reasoning Error Disruption Attack of LLMs (ACL 2025) [arxiv]
• FLUID-MMRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation (KDD 2025) [arxiv]
• Optimal Policy Adaptation under Covariate Shift (IJCAI 2025) [arxiv]
• DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation (IJCAI 2025)[arxiv]
• STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation (SIGIR 2025) [arxiv]

View all publications

Interested in Causal ML? Please find our papers and algorithm/data repositories.

• Survey Papers
  • Causal Interpretability for Machine Learning–Problems, Methods and Evaluation
  • A Survey of Learning Causality with Data: Problems and Methods
• Repositories
  • Algorithm Repository: Awesome-Causality-Algorithms
  • Data Repository: Awesome-Causality-Data

Experience:

• Staff Research Scientist, Intuit AI Research, March 2025 -
• Senior Machine Learning Researcher, ByteDance Research, June 2022 - Jan 2025
• Assistant Professor, City University of Hong Kong, Aug 2021 - June 2022
• AI Resident, Google X, Aug 2020 - Dec 2020
  • Mentor: Hongxu Ma
• Research Intern, Microsoft Research, May 2020 - Aug 2020
  • Mentor: Emre Kiciman, Pengchuan Zhang
• Research Intern, Etsy, May 2019 - Aug 2019
  • Mentor: Liangjie Hong, Xiaoting Zhao, Adam Henderson

Students and Interns (and papers co-authored with me)

• Maolin Wang (PhD Student@City University of Hong Kong, Co-supervised w. Xiangyu Zhao and Junhui Wang)
  • ACL’25, KDD’25, SIGIR’25, IJCAI’25, WWW’24, SDM’24, ICDM’23
• Zonghao Chen (PhD Student@UCL, Intern@ByteDance Research)
  • KDD’24
• Tongxin Yin (PhD Student@UMich, Intern@ByteDance Research)
  • ICLR’24
• Qing Zhang (PhD Student@HKUST, Intern@ByteDance Research)
  • KDD’23
• Xiaohui Chen (PhD Student@Tufts, Intern@ByteDance)
  • KDD’23
• Chentao Cao (PhD Student@HKBU, Intern@ByteDance Research)
• Sichun Luo (PhD Student@CityU, Intern@ByteDance)
• Xinjian Zhao (Master Student@CityU -> PhD Student@CUHK SZ)
• Jiansheng Li (Master Student@CityU -> PhD Student@CUHK SZ)