Luo, Di

13 publications

ICLR 2026 A Universal Compression Theory for Lottery Ticket Hypothesis and Neural Scaling Laws Hong-Yi Wang, Di Luo, Tomaso Poggio, Isaac L. Chuang, Liu Ziyin

ICLR 2026 CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers Haining Pan, James V Roggeveen, Erez Berg, Juan Felipe Carrasquilla Alvarez, Debanjan Chowdhury, Surya Ganguli, Federico Ghimenti, Juraj Hasik, Henry S. Hunt, Hong-Chen Jiang, Mason Kamb, Ying-Jer Kao, Ehsan Khatami, Michael J Lawler, Di Luo, Titus Neupert, Xiaoliang Qi, Michael Brenner, Eun-Ah Kim

NeurIPS 2025 L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling Zhuo Chen, Oriol Mayné i Comas, Zhuotao Jin, Di Luo, Marin Soljacic

AAAI 2024 Enhancing Job Recommendation Through LLM-Based Generative Adversarial Networks Yingpeng Du, Di Luo, Rui Yan, Xiaopei Wang, Hongzhi Liu, Hengshu Zhu, Yang Song, Jie Zhang

NeurIPS 2024 QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljačić

NeurIPS 2024 SciCode: A Research Coding Benchmark Curated by Scientists Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, Xinan Chen, Cunwei Fan, Xuefei Guo, Roland Haas, Pan Ji, Kittithat Krongchon, Yao Li, Shengyan Liu, Di Luo, Yutao Ma, Hao Tong, Kha Trinh, Chenyu Tian, Zihan Wang, Bohao Wu, Yanyu Xiong, Shengzhu Yin, Minhui Zhu, Kilian Lieret, Yanxin Lu, Genglin Liu, Yufeng Du, Tianhua Tao, Ofir Press, Jamie Callan, Eliu Huerta, Hao Peng

ICML 2024 TENG: Time-Evolving Natural Gradient for Solving PDEs with Deep Neural Nets Toward Machine Precision Zhuo Chen, Jacob Mccarran, Esteban Vizcaino, Marin Soljacic, Di Luo

NeurIPS 2023 ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo, Marin Soljacic

NeurIPS 2023 Lift Yourself up: Retrieval-Augmented Text Generation with Self-Memory Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, Rui Yan

ICML 2023 Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows Owen M Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic

NeurIPS 2023 QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

NeurIPSW 2022 Data-Driven Acceleration of Quantum Optimization and Machine Learning via Koopman Operator Learning Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljacic

NeurIPSW 2022 Gauge Equivariant Neural Networks for 2+1d U(1) Gauge Theory Simulations in Hamiltonian Formulation Di Luo, Shunyue Yuan, James Stokes, Bryan Clark