Publications (40)
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation
Zixuan Wang, Yuhong Chen, Yuxuan Zhu +10
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and h…
Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views
Tingyang Chen, Cong Fu, Jiahua Wu +6
Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from…
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu +13
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain…
Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks
Shuntian Zheng, Jiawei Wang, Cong Fu +4
Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems
Yuxuan Zhu, Cong Fu, Yabo Ni +2
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to ca…
Narrow Linewidth Laser Based on Extended Topological Interface States in One-Dimensional Photonic Crystals
Xiao Sun, Zhibo Li, Yiming Sun +7
Recent advances in topological one-dimensional photonic crystal concepts have enabled the development of robust light-emitting devices by incorporating a topological interface stat…
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph
Cong Fu, Chao Xiang, Changxu Wang +1
Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some early gra…
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning
Wenxiao Wang, Cong Fu, Jishun Guo +2
Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art appr…
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
Cong Fu, Deng Cai
Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical s…
A Latent Diffusion Model for Protein Structure Generation
Cong Fu, Keqiang Yan, Limei Wang +7
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synt…
Maximum Inner Product is Query-Scaled Nearest Neighbor
Tingyang Chen, Cong Fu, Kun Wang +5
Maximum Inner Product Search (MIPS) for high-dimensional vectors is pivotal across databases, information retrieval, and artificial intelligence. Existing methods either reduce MIP…
OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation
Jiakai Tang, Sunhao Dai, Kun Wang +8
Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs…
Toward less conservative distributed stability analysis of power systems via matrix-valued differential passivity indices
Xi Ru, Cong Fu, Zhongze Li +2
Passivity indices have been widely adopted to derive distributed stability certificates for power systems. Nevertheless, conventional passivity indices remain scalar-valued even fo…
MedCT: A Clinical Terminology Graph for Generative AI Applications in Healthcare
Ye Chen, Dongdong Huang, Haoyun Xu +5
We introduce the world's first clinical terminology for the Chinese healthcare community, namely MedCT, accompanied by a clinical foundation model MedBERT and an entity linking mod…
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian +2
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crys…
Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +6
Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obt…
Unified approach to power-efficiency trade-off relations of generic thermal machines
Yu-Han Ma, Cong Fu
We present a general framework for determining the power-efficiency trade-off relations across arbitrary thermal machines, addressing the lack of unified optimization results stemm…
A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +8
Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theor…
Unlocking inaccessible performance of the quantum refrigerator with catalysts
Cong Fu, Ousi Pan, Zhiqiang Fan +4
Quantum thermal machines offer promising platforms for exploring the fundamental limits of thermodynamics at the microscopic scale. The previous study demonstrated that the incorpo…
Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering
Zhongjin Zhang, Yu Liang, Cong Fu +5
Embedding-based collaborative filtering, often coupled with nearest neighbor search, is widely deployed in large-scale recommender systems for personalized content selection. Moder…
Residual Multi-Task Learner for Applied Ranking
Cong Fu, Kun Wang, Jiahua Wu +5
Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their…
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
Lianhao Zhou, Hongyi Ling, Cong Fu +14
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous sys…
Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models
Cong Fu, Xiner Li, Blake Olson +2
Structure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand inter…
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
Xuan Zhang, Jacob Helwig, Yuchao Lin +4
We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dy…
Group Equivariant Fourier Neural Operators for Partial Differential Equations
Jacob Helwig, Xuan Zhang, Cong Fu +3
We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on th…
High Dimensional Similarity Search with Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility
Cong Fu, Changxu Wang, Deng Cai
Approximate Nearest Neighbor Search (ANNS) in high dimensional space is essential in database and information retrieval. Recently, there has been a surge of interest in exploring e…
Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
Yu Liang, Zhongjin Zhang, Yuxuan Zhu +10
Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item emb…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation
Kun Yang, Yuxuan Zhu, Yazhe Chen +7
Sequential recommendation increasingly employs latent multi-step reasoning to enhance test-time computation. Despite empirical gains, existing approaches largely drive intermediate…
Non-monotonic Irreversibility in Polytropic Steering
Cong Fu, Youhui Lin, Shanhe Su +1
The efficient manipulation of thermodynamic states within the finite time is fundamentally constrained by the intrinsic dissipative cost. While the slow-driving regime is well-char…
SoftTiger: A Clinical Foundation Model for Healthcare Workflows
Ye Chen, Igor Couto, Wei Cai +2
We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows. The narrative and unstructured nature of clinical notes is a…
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Tingyang Chen, Cong Fu, Xiangyu Ke +3
Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approache…
Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems
Cong Fu, Xuan Zhang, Huixin Zhang +3
Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems. Existing methods use convolutional neural networks (…
Quantum Coherence as a Thermodynamic Resource Beyond the Classical Uncertainty Bound
Shanhe Su, Cong Fu, Ousi Pan +3
Thermodynamic uncertainty relations (TURs) establish a fundamental trade-off between current precision and entropy production in nonequilibrium systems, yet the role of genuine qua…
Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
Yuchao Lin, Cong Fu, Zachary Krueger +6
-equivariant networks are the dominant models for machine learning interatomic potentials (MLIPs). The key operation of such networks is the Clebsch-Gordan (CG) tensor…
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
TigerBot: An Open Multilingual Multitask LLM
Ye Chen, Wei Cai, Liangmin Wu +3
We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters. We develop our mo…
Collaborative Policy Learning for Open Knowledge Graph Reasoning
Cong Fu, Tong Chen, Meng Qu +2
In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse an…