Binhang Yuan

According to our database, Binhang Yuan authored at least 71 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Re:Form - Reducing Human Priors in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny.

Chuanhao Yan, Fengdi Che, Xuhan Huang, Xu Xu, Xin Li, Yizhi Li, Xingwei Qu, Jingzhe Shi, Zhuangzhuang He, Chenghua Lin, Yaodong Yang, Binhang Yuan, Hang Zhao, Yu Qiao, Bowen Zhou, Jie Fu

CoRR, July, 2025

Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification.

Tianyi Bai, Zengjie Hu, Fupeng Sun, Jiantao Qiu, Yizhen Jiang, Guangxin He, Bohan Zeng, Conghui He, Binhang Yuan, Wentao Zhang

CoRR, June, 2025

Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning.

Tianyi Bai, Yuxuan Fan, Jiantao Qiu, Fupeng Sun, Jiayi Song, Junlin Han, Zichen Liu, Conghui He, Wentao Zhang, Binhang Yuan

CoRR, June, 2025

Cascadia: A Cascade Serving System for Large Language Models.

Youhe Jiang, Fangcheng Fu, Wanru Zhao, Stephan Rabanser, Nicholas D. Lane, Binhang Yuan

CoRR, June, 2025

TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network.

Guangxin He, Yuan Cao, Yutong He, Tianyi Bai, Kun Yuan, Binhang Yuan

CoRR, June, 2025

AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning.

Wei Fu, Jiaxuan Gao, Xujie Shen, Chen Zhu, Zhiyu Mei, Chuyi He, Shusheng Xu, Guo Wei, Jun Mei, Jiashu Wang, Tongkai Yang, Binhang Yuan, Yi Wu

CoRR, May, 2025

HEXGEN-TEXT2SQL: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL Workflow.

You Peng, Youhe Jiang, Chen Wang, Binhang Yuan

CoRR, May, 2025

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models.

Guanduo Chen, Yutong He, Yipeng Hu, Kun Yuan, Binhang Yuan

CoRR, February, 2025

AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model for Atmospheric Science.

Chenyue Li, Wen Deng, Mengqian Lu, Binhang Yuan

CoRR, February, 2025

Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs.

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, Eiko Yoneki

CoRR, February, 2025

Top Ten Challenges Towards Agentic Neural Graph Databases.

Jiaxin Bai, Zihao Wang, Yukun Zhou, Hang Yin, Weizhi Fei, Qi Hu, Zheye Deng, Jiayang Cheng, Tianshi Zheng, Hong Ting Tsang, Yisen Gao, Zhongwei Xie, Yufei Li, Lixin Fan, Binhang Yuan, Wei Wang, Lei Chen, Xiaofang Zhou, Yangqiu Song

IEEE Data Eng. Bull., 2025

Toppings: CPU-Assisted, Rank-Aware Adapter Serving for LLM Inference.

Suyi Li, Hanfeng Lu, Tianyuan Wu, Minchen Yu, Qizhen Weng, Xusheng Chen, Yizhou Shan, Binhang Yuan, Wei Wang

Proceedings of the 2025 USENIX Annual Technical Conference, 2025

Prompt Inversion Attack Against Collaborative Inference of Large Language Models.

Wenjie Qu, Yuguang Zhou, Yongji Wu, Tingsong Xiao, Binhang Yuan, Yiming Li, Jiaheng Zhang

Proceedings of the IEEE Symposium on Security and Privacy, 2025

DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference.

Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, Tao Lin

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment.

Youhe Jiang, Ran Yan, Binhang Yuan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Ratel: Optimizing Holistic Data Movement to Fine-tune 100B Model on a Consumer GPU.

Changyue Liao, Mo Sun, Zihan Yang, Jun Xie, Kaiqi Chen, Binhang Yuan, Fei Wu, Zeke Wang

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage.

Yongjun He, Roger Waleffe, Zhichao Han, Johnu George, Binhang Yuan, Zitao Zhang, Yinan Shan, Yang Zhao, Debojyoti Dutta, Theodoros Rekatsinas, Ce Zhang

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration.

Tianyi Bai, Ling Yang, Zhen Hao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Jiantao Qiu, Wentao Zhang, Binhang Yuan, Conghui He

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systems.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

VLDB J., September, 2024

TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension.

Zipeng Qiu, You Peng, Guangxin He, Binhang Yuan, Chen Wang

CoRR, 2024

Zero-Indexing Internet Search Augmented Generation for Large Language Models.

Guangxin He, Zonghong Dai, Jiangcheng Zhu, Binqiang Zhao, Chenyue Li, You Peng, Chen Wang, Binhang Yuan

CoRR, 2024

Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining.

Tianyi Bai, Ling Yang, Zhen Hao Wong, Jiahui Peng, Xinlin Zhuang, Chi Zhang, Lijun Wu, Jiantao Qiu, Wentao Zhang, Binhang Yuan, Conghui He

CoRR, 2024

Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads.

Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, Zhiyuan Liu

CoRR, 2024

FlashFlex: Accommodating Large Language Model Training over Heterogeneous Environment.

Ran Yan, Youhe Jiang, Wangcheng Tao, Xiaonan Nie, Bin Cui, Binhang Yuan

CoRR, 2024

On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study.

Lujia Zhang, Hanzhe Cui, Yurong Song, Chenyue Li, Binhang Yuan, Mengqian Lu

CoRR, 2024

A Survey of Multimodal Large Language Model from A Data-centric Perspective.

Tianyi Bai, Hao Liang, Binwang Wan, Ling Yang, Bozhou Li, Yifan Wang, Bin Cui, Conghui He, Binhang Yuan, Wentao Zhang

CoRR, 2024

Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model.

Xinrun Du, Zhouliang Yu, Songyang Gao, Ding Pan, Yuyang Cheng, Ziyang Ma, Ruibin Yuan, Xingwei Qu, Jiaheng Liu, Tianyu Zheng, Xinchen Luo, Guorui Zhou, Binhang Yuan, Wenhu Chen, Jie Fu, Ge Zhang

CoRR, 2024

DeFT: Flash Tree-attention with IO-Awareness for Efficient Tree-search-based LLM Inference.

Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, Tao Lin

CoRR, 2024

Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU.

Changyue Liao, Mo Sun, Zihan Yang, Kaiqi Chen, Binhang Yuan, Fei Wu, Zeke Wang

CoRR, 2024

CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference.

Suyi Li, Hanfeng Lu, Tianyuan Wu, Minchen Yu, Qizhen Weng, Xusheng Chen, Yizhou Shan, Binhang Yuan, Wei Wang

CoRR, 2024

Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild.

Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training.

Lin Lu, Chenxi Dai, Wangcheng Tao, Binhang Yuan, Yanan Sun, Pan Zhou

Proceedings of the Forty-first International Conference on Machine Learning, 2024

HexGen: Generative Inference of Large Language Model over Heterogeneous Environment.

Youhe Jiang, Ran Yan, Xiaozhe Yao, Yang Zhou, Beidi Chen, Binhang Yuan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Serving Deep Learning Models from Relational Databases.

Lixi Zhou, Qi Lin, Kanchan Chowdhury, Saif Masood, Alexandre E. Eichenberger, Hong Min, Alexander Sim, Jie Wang, Yida Wang, Kesheng Wu, Binhang Yuan, Jia Zou

Proceedings of the Proceedings 27th International Conference on Extending Database Technology, 2024

2023

Holistic Evaluation of Language Models.

Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda

Trans. Mach. Learn. Res., 2023

Exploring the Robustness of Decentralized Training for Large Language Models.

Lin Lu, Chenxi Dai, Wangcheng Tao, Binhang Yuan, Yanan Sun, Pan Zhou

CoRR, 2023

HexGen: Generative Inference of Foundation Model over Heterogeneous Decentralized Environment.

Youhe Jiang, Ran Yan, Xiaozhe Yao, Beidi Chen, Binhang Yuan

CoRR, 2023

Serving Deep Learning Model in Relational Databases.

Alexandre E. Eichenberger, Qi Lin, Saif Masood, Hong Min, Alexander Sim, Jie Wang, Yida Wang, Kesheng Wu, Binhang Yuan, Lixi Zhou, Jia Zou

CoRR, 2023

High-throughput Generative Inference of Large Language Models with a Single GPU.

Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark W. Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

CoRR, 2023

CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks.

Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang

Proceedings of the International Conference on Machine Learning, 2023

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning.

Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine

Proceedings of the International Conference on Machine Learning, 2023

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time.

Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen

Proceedings of the International Conference on Machine Learning, 2023

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU.

Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

Proceedings of the International Conference on Machine Learning, 2023

2022

Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences.

Huaqing Jin, Guosheng Yin, Binhang Yuan, Fei Jiang

Technometrics, 2022

Distributed Learning of Fully Connected Neural Networks using Independent Subnet Training.

Binhang Yuan, Cameron R. Wolfe, Chen Dun, Yuxin Tang, Anastasios Kyrillidis, Chris Jermaine

Proc. VLDB Endow., 2022

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Zijian Ding, Guijin Wang, Huazhong Yang, Ping Zhang, Dapeng Fu, Zhen Yang, Xinkang Wang, Xia Wang, Zhourui Xia, Chiming Zhang, Wenjie Cai, Binhang Yuan, Dongya Jia, Bo Chen, Chengbin Huang, Jing Zhang, Yi Li, Shan Yang, Runnan He

Medical Biol. Eng. Comput., 2022

Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

CoRR, 2022

Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees.

Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang

CoRR, 2022

In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle.

Lijie Xu, Shuang Qiu, Binhang Yuan, Jiawei Jiang, Cédric Renggli, Shaoduo Gan, Kaan Kara, Guoliang Li, Ji Liu, Wentao Wu, Jieping Ye, Ce Zhang

Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Decentralized Training of Foundation Models in Heterogeneous Environments.

Binhang Yuan, Yongjun He, Jared Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Ré, Ce Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees.

Jue Wang, Binhang Yuan, Luka Rimanic, Yongjun He, Tri Dao, Beidi Chen, Christopher Ré, Ce Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters.

Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Efficient flow scheduling in distributed deep learning training with echelon formation.

Rui Pan, Yiming Lei, Jialong Li, Zhiqiang Xie, Binhang Yuan, Yiting Xia

Proceedings of the 21st ACM Workshop on Hot Topics in Networks, 2022

2021

A Feature Fusion Framework and Its Application to Automatic Seizure Detection.

Chengbin Huang, Weiting Chen, Mingsong Chen, Binhang Yuan

IEEE Signal Process. Lett., 2021

Lachesis: Automated Partitioning for UDF-Centric Analytics.

Jia Zou, Amitabh Das, Pratik Barhate, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chris Jermaine

Proc. VLDB Endow., 2021

Tensor Relational Algebra for Distributed Machine Learning System Design.

Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, Chris Jermaine

Proc. VLDB Endow., 2021

Distributed Numerical and Machine Learning Computations via Two-Phase Execution of Aggregated Join Trees.

Dimitrije Jankov, Binhang Yuan, Shangyu Luo, Chris Jermaine

Proc. VLDB Endow., 2021

BAGUA: Scaling up Distributed Learning with System Relaxations.

Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang

Proc. VLDB Endow., 2021

Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra.

Shangyu Luo, Dimitrije Jankov, Binhang Yuan, Chris Jermaine

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020

Declarative Recursive Computation on an RDBMS: or, Why You Should Use a Database For Distributed Machine Learning.

Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao

SIGMOD Rec., 2020

Tensor Relational Algebra for Machine Learning System Design.

Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, Chris Jermaine

CoRR, 2020

Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications.

Jia Zou, Pratik Barhate, Amitabh Das, Arun Iyengar, Binhang Yuan, Dimitrije Jankov, Chris Jermaine

CoRR, 2020

A Federated Learning Framework for Healthcare IoT devices.

Binhang Yuan, Song Ge, Wenhui Xing

CoRR, 2020

2019

Declarative Recursive Computation on an RDBMS.

Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao

Proc. VLDB Endow., 2019

Distributed Learning of Deep Neural Networks using Independent Subnet Training.

Binhang Yuan, Anastasios Kyrillidis, Christopher M. Jermaine

CoRR, 2019

WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection.

Binhang Yuan, Chen Wang, Fei Jiang, Mingsheng Long, Philip S. Yu, Yuan Liu

CoRR, 2019

Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks.

Binhang Yuan, Wenhui Xing

Proceedings of the Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting, 2019

2018

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development.

Jia Zou, R. Matthew Barnett, Tania Lorido-Botran, Shangyu Luo, Carlos Monroy, Sourav Sikdar, Kia Teymourian, Binhang Yuan, Chris Jermaine

Proceedings of the 2018 International Conference on Management of Data, 2018

2017

Abridging source code.

Binhang Yuan, Vijayaraghavan Murali, Christopher M. Jermaine

Proc. ACM Program. Lang., 2017

2016

Generating a 3D Normative Infant Cranial Model.

Binhang Yuan, Ronald N. Goldman, Eric Wang, Olushola Olorunnipa, David Khechoyan

Proceedings of the International Conference on Computational Science 2016, 2016

2014

Effective Video Retargeting With Jittery Assessment.

Bo Yan, Binhang Yuan, Bo Yang

IEEE Trans. Multim., 2014