Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction. - Xili Dai; Shengbang Tong; Mingyang Li; Ziyang Wu; Kwan Ho Ryan Chan; Pengyuan Zhai; Yaodong Yu; Michael Psenka; Xiaojun Yuan 002; Heung-Yeung Shum; Yi Ma 001 (CoRR, 2021)

Revisiting Sparse Convolutional Model for Visual Recognition. - Xili Dai; Mingyang Li; Pengyuan Zhai; Shengbang Tong; Xingjian Gao; Shao-Lun Huang; Zhihui Zhu; Chong You; Yi Ma 001 (NeurIPS, 2022)

Unsupervised Learning of Structured Representations via Closed-Loop Transcription. - Shengbang Tong; Xili Dai; Yubei Chen; Mingyang Li; Zengyi Li; Brent Yi; Yann LeCun; Yi Ma 001 (CoRR, 2022)

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction. - Xili Dai; Shengbang Tong; Mingyang Li; Ziyang Wu; Michael Psenka; Kwan Ho Ryan Chan; Pengyuan Zhai; Yaodong Yu; Xiaojun Yuan 002; Heung-Yeung Shum; Yi Ma 001 (Entropy, 2022)

Unsupervised Manifold Linearizing and Clustering. - Tianjiao Ding; Shengbang Tong; Kwan Ho Ryan Chan; Xili Dai; Yi Ma 001; Benjamin D. Haeffele (ICCV, 2023)

Incremental Learning of Structured Memory via Closed-Loop Transcription. - Shengbang Tong; Xili Dai; Ziyang Wu; Mingyang Li; Brent Yi; Yi Ma 001 (ICLR, 2023)

Mass-Producing Failures of Multimodal Systems with Language Models. - Shengbang Tong; Erik Jones; Jacob Steinhardt (NeurIPS, 2023)

White-Box Transformers via Sparse Rate Reduction. - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Benjamin D. Haeffele; Yi Ma 001 (NeurIPS, 2023)

EMP-SSL: Towards Self-Supervised Learning in One Training Epoch. - Shengbang Tong; Yubei Chen; Yi Ma 001; Yann LeCun (CoRR, 2023)

Investigating the Catastrophic Forgetting in Multimodal Large Language Models. - Yuexiang Zhai; Shengbang Tong; Xiao Li 022; Mu Cai; Qing Qu 001; Yong Jae Lee; Yi Ma 001 (CoRR, 2023)

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Hao Bai; Yuexiang Zhai; Benjamin D. Haeffele; Yi Ma 001 (CoRR, 2023)

Closed-Loop Transcription via Convolutional Sparse Coding. - Xili Dai; Ke Chen; Shengbang Tong; Jingyuan Zhang; Xingjian Gao; Mingyang Li; Druv Pai; Yuexiang Zhai; Xiaojun Yuan 002; Heung-Yeung Shum; Lionel M. Ni; Yi Ma 001 (CPAL, 2024)

Unsupervised Learning of Structured Representation via Closed-Loop Transcription. - Shengbang Tong; Xili Dai; Yubei Chen; Mingyang Li; Zengyi Li; Brent Yi; Yann LeCun; Yi Ma 001 (CPAL, 2024)

Emergence of Segmentation with Minimalistic White-Box Transformers. - Yaodong Yu; Tianzhe Chu; Shengbang Tong; Ziyang Wu; Druv Pai; Sam Buchanan; Yi Ma 001 (CPAL, 2024)

Investigating the Catastrophic Forgetting in Multimodal Large Language Model Fine-Tuning. - Yuexiang Zhai; Shengbang Tong; Xiao Li 022; Mu Cai; Qing Qu 001; Yong Jae Lee; Yi Ma 001 (CPAL, 2024)

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs. - Shengbang Tong; Zhuang Liu 003; Yuexiang Zhai; Yi Ma 001; Yann LeCun; Saining Xie (CVPR, 2024)

Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models. - Tianzhe Chu; Shengbang Tong; Tianjiao Ding; Xili Dai; Benjamin David Haeffele; René Vidal; Yi Ma 001 (ICLR, 2024)

Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription. - Hongxiang Zhao; Xili Dai; Jianan Wang; Shengbang Tong; Jingyuan Zhang; Weida Wang; Lei Zhang 001; Yi Ma 001 (CoRR, 2024)

Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning. - Yuexiang Zhai; Hao Bai; Zipeng Lin; Jiayi Pan; Shengbang Tong; Yifei Zhou; Alane Suhr; Saining Xie; Yann LeCun; Yi Ma 001; Sergey Levine (CoRR, 2024)

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs. - Shengbang Tong; Ellis Brown; Penghao Wu; Sanghyun Woo; Manoj Middepogu; Sai Charitha Akula; Jihan Yang; Shusheng Yang; Adithya Iyer; Xichen Pan; Austin Wang; Rob Fergus; et al. (CoRR, 2024)

Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning. - Shentong Mo; Shengbang Tong (CoRR, 2024)

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning. - Shengbang Tong; David Fan; Jiachen Zhu 002; Yunyang Xiong; Xinlei Chen; Koustuv Sinha; Michael Rabbat; Yann LeCun; Saining Xie; Zhuang Liu 003 (CoRR, 2024)

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark. - Xiang Yue; Tianyu Zheng; Yuansheng Ni; Yubo Wang; Kai Zhang 033; Shengbang Tong; Yuxuan Sun 002; Botao Yu; Ge Zhang 009; Huan Sun 001; Yu Su 001; Wenhu Chen; et al. (ACL (1), 2025)

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training. - Tianzhe Chu; Yuexiang Zhai; Jihan Yang; Shengbang Tong; Saining Xie; Dale Schuurmans; Quoc V. Le; Sergey Levine; Yi Ma 001 (CoRR, 2025)

Scaling Language-Free Visual Representation Learning. - David Fan; Shengbang Tong; Jiachen Zhu 002; Koustuv Sinha; Zhuang Liu 003; Xinlei Chen; Michael Rabbat; Nicolas Ballas; Yann LeCun; Amir Bar; Saining Xie (CoRR, 2025)

Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs. - Chun-Hsiao Yeh; Chenyu Wang; Shengbang Tong; Ta Ying Cheng; Ruoyu Wang 014; Tianzhe Chu; Yuexiang Zhai; Yubei Chen; Shenghua Gao; Yi Ma 001 (CoRR, 2025)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. - Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang 008; Tong Zhang 001; Ameet Talwalkar; Aviral Kumar (CoRR, 2025)

From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models. - Irving Fang; Juexiao Zhang; Shengbang Tong; Chen Feng 002 (CoRR, 2025)