Publication List
(A superscript * denotes equal contribution)-
Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal ControlPeihao Wang, Shan Yang, Xijun Wang, Tesi Xiao, Xin Liu, Changlong Yu, Yu Lou, Pan Li, Atlas Wang, Ming Lin, Rene Vidalpreprint, 2026We propose the Test-Time Control (TTC) for LLM reasoning, which formulates iterative reasoning as an optimal control problem solvable with hardware-efficient algorithms.[Paper] [Code]
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VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D ReconstructionZhiwen Fan, Jian Zhang, Renjie Li, Junge Zhang, Runjin Chen, Hezhen Hu, Kevin Wang, Peihao Wang, Huaizhi Qu, Shijie Zhou, Dilin Wang, Zhicheng Yan, Hongyu Xu, Justin Theiss, Tianlong Chen, Jiachen Li, Zhengzhong Tu, Zhangyang Wang, Rakesh RanjanIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026We proposed a unified Vision-Language Model (VLM) framework integrating 3D reconstructive instruction tuning for deep spatial understanding from monocular video.[Project Page] [Paper] [Code]
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∇-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent SpaceInternational Conference on Learning Representations (ICLR), 2026We propose ∇-reasoner, an iterative decoding approach with policy refinement by test-time gradient descent on textual representations to improve LLM reasoning.[Paper] [Code]
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Understanding the Mixture-of-Experts with Nadaraya-Watson KernelChuanyang Zheng, Jiankai Sun, Yihang Gao, Enze Xie, Yuehao Wang, Peihao Wang, Ting Xu, Matthew Chan, Liliang Ren, Jingyao Li, Jing Xiong, Kashif Rasul, Mac Schwager, Anderson Schneider, Atlas Wang, Yuriy Nevmyvaka,International Conference on Learning Representations (ICLR), 2026We rethink the MoE with Nadaraya-Watson Kernel and propose KERN router to replace Softmax router to achieve better performance.[Paper]
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Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language ModelsAdvances in Neural Information Processing Systems (NeurIPS), 2025We introduce Graph-KV, a zero-shot method that injects graph structures into large language models to improve long-context, multi-hop, and retrieval-augmented reasoning.[Paper] [Code]
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SAS: Simulated Attention ScoreChuanyang Zheng, Jiankai Sun, Yihang Gao, Yuehao Wang, Peihao Wang, Jing Xiong, Liliang Ren, Hao Cheng, Janardhan Kulkarni, Yelong Shen, Atlas Wang, Mac Schwager, Anderson Schneider, Xiaodong Liu, Jianfeng GaoAdvances in Neural Information Processing Systems (NeurIPS), 2025We introduce Simulated Attention Score (SAS), which boosts LLM performance by "simulating" a larger number of attention heads and hidden features with a compact model.[Paper]
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Drag-and-Drop LLMs: Zero-Shot Prompt-to-WeightsZhiyuan Liang, Dongwen Tang, Yuhao Zhou, Xuanlei Zhao, Mingjia Shi, Wangbo Zhao, Zekai Li, Peihao Wang, Konstantin Schürholt, Damian Borth, Michael Bronstein, Yang You, Atlas Wang, Kai WangAdvances in Neural Information Processing Systems (NeurIPS), 2025We develop Drag-and-Drop LLMs, a zero-shot prompt-to-weights approach that enables on-the-fly model adaptation without fine-tuning.[Project Page] [Paper] [Code]
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CryoFastAR: Fast Cryo-EM Ab Initio Reconstruction Made EasyIEEE International Conference on Computer Vision (ICCV), 2025We train an end-to-end reconstruction network that recovers 3D protein structures from low SNR cryogenic electronic images in seconds.[Paper]
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Rethinking Addressing in Language Models via Contextualized Equivariant Positional EncodingInternational Conference on Machine Learning (ICML), 2025We enhance the reasoning ability of large language models via contextualizing positional encoding with equivariance constraints.[Paper] [Code]
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Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic ReasoningPeihao Wang*, Atlas Wang*International Conference on Neuro-symbolic Systems (NeuS), 2025, Disruptive Idea AwardsWe theoretically demonstrate that symbolic structures are inherent in neural weight space and gradient descent under geometric constraints can find these solutions.[Paper]
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Steepest Descent Density Control for Compact 3D Gaussian SplattingPeihao Wang*, Yuehao Wang*, Dilin Wang, Sreyas Mohan, Zhiwen Fan, Lemeng Wu, Ruisi Cai, Yu-Ying Yeh, Atlas Wang, Qiang Liu, Rakesh RanjanIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025We demystify the role of densification for Gaussian splatting in escaping saddle points during optimization and propose an optimal density control strategy for the steepest loss descent.[Project Page] [Paper] [Code]
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FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian SplattingHengyu Liu*, Yuehao Wang*, Chenxin Li*, Ruisi Cai, Kevin Wang, Wuyang Li, Pavlo Molchanov, Peihao Wang, Atlas WangIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025We present an elastic inference method for 3DGS that requires one-time training while enabling an adaptive subset selection of point clouds for dynamic inference-time budgets.[Project Page] [Paper] [Code]
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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothingInternational Conference on Learning Representations (ICLR), 2025We theoretically and empirically reveal and tackle locality and over-smoothing bottlenecks for state space models.[Paper] [Code]
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SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein IdentityPeihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Atlas Wang, Vikas ChandraInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025We adopt control variate method constructed by Stein identity to reduce variance in Monte Carlo estimation for text-to-3D score distillation.[MarkTechPost] [Project Page] [Paper] [Code]
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READ-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-DesignRuisi Cai*, Yeonju Ro*, Geon-Woo Kim, Peihao Wang, Babak Ehteshami Bejnordi, Aditya Akella, Atlas WangAdvances in Neural Information Processing Systems (NeurIPS), 2024We refactorize LLMs to MoE models with a system-friendly pre-computed routing policy.[Paper] [Code]
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Large Spatial Model: Real-time Unposed Images to Semantic 3DZhiwen Fan*, Jian Zhang*, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen, Shijie Zhou, Achuta Kadambi, Atlas Wang, Danfei Xu, Boris Ivanovic, Marco Pavone, Yue WangAdvances in Neural Information Processing Systems (NeurIPS), 2024We propose Large Spatial Model, an all-in-one pipeline accomplishing uncalibrated 3D reconstruction, understanding, and interaction in real time.[Project Page] [Paper] [Code]
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Perspective-Aligned AR Mirror with Under-Display CameraJian Wang, Sizhuo Ma, Karl Bayer, Yi Zhang, Peihao Wang, Bing Zhou, Shree K. Nayar, Gurunandan KrishnanSIGGRAPH Asia (Journal Track), ACM Transactions on Graphics, 2024, Best Paper AwardWe propose an efficient learning-based algorithm that restores images formed by under-display cameras.[Award Press] [Paper]
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VersatileGaussian: Real-time Neural Rendering for Versatile Tasks using Gaussian SplattingEuropean Conference on Computer Vision (ECCV), 2024We enable multi-task representation learning with 3D Gaussian Splatting, which achieves real-time multi-task decoding for novel views.[Project Page] [Paper]
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Taming Mode Collapse in Score Distillation for Text-to-3D GenerationPeihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Atlas Wang, Vikas ChandraIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024We derive an entropy-maximizing score distillation rule that fosters view diversity and addresses the multi-face problem for text-to-3D generation.[Project Page] [Paper] [Code]
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Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3DIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024We propose Lift3D that lifts arbitrary 2D backbone to generate view-consistent predictions without any retraining.[Project Page] [Paper] [Code]
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Polynomial Width is Sufficient for Set Representation with High-dimensional FeaturesInternational Conference on Learning Representations (ICLR), 2024We theoretically show that polynomial many neurons are sufficient for set representation with DeepSets architecture.[Paper]
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Patch Diffusion: Faster and More Data-Efficient Training of Diffusion ModelsZhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Atlas Wang, Weizhu Chen, Mingyuan ZhouAdvances in Neural Information Processing Systems (NeurIPS), 2023We show that diffusion models can be well trained with merely image patches, which reduces training costs while improving data efficiency.[Paper] [Code]
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Vision HGNN: An Image is More than a Graph of NodesYan Han, Peihao Wang, Souvik Kundu, Ying Ding, Atlas WangIEEE International Conference on Computer Vision (ICCV), 2023, OralIn this paper, we model an image with a hypergraph to capture high-order interactions, and further employ Hypergraph Neural Networks (HGNN) for learning representations.[Paper] [Code]Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-ExpertsWenyan Cong*, Hanxue Liang*, Peihao Wang, Zhiwen Fan, Tianlong Chen, Mukund Varma T, Yi Wang, Atlas WangIEEE International Conference on Computer Vision (ICCV), 2023We scale up and improve the generalization ability of Generalizable NeRF Transformer (GNT) via plugging in the Mixture-of-Expert (MoE) layers.[Paper] [Code]Data Efficient Neural Scaling Law via Model ReusingPeihao Wang, Rameswar Panda, Atlas WangInternational Conference on Machine Learning (ICML), 2023We empirically reveal that the existing power laws under-estimate the data inefficiency of large transformers, and leverage model reusing to reproduce the power law under the data scarcity regime.[Paper] [Code]NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360 ViewsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, HighlightWe study how to lift a single image to a 3D object and generate its 360° views that correspond well with the given reference image.[Project Page] [Paper] [Code]Equivariant Hypergraph Diffusion Neural OperatorsInternational Conference on Learning Representations (ICLR), 2023This work proposes a new hypergraph neural network architecture, which provably represents any continuous equivariant hypergraph diffusion operators.[Paper] [Code]Is Attention All That NeRF Needs?International Conference on Learning Representations (ICLR), 2023We present Generalizable NeRF Transformer (GNT), a pure, unified transformer-based architecture that efficiently reconstructs Neural Radiance Fields (NeRFs) on the fly.[Project Page] [Paper] [Code]NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex ScenesInternational Conference on Learning Representations (ICLR), 2023We propose a novel collaborative contrastive loss for NeRF to segment objects in complex real-world scenes, without any annotation.[Project Page] [Paper] [Code]Learning to Grow Pretrained Models for Efficient Transformer TrainingPeihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogerio Feris, David Cox, Atlas Wang, Yoon KimInternational Conference on Learning Representations (ICLR), 2023, SpotlightThis paper proposes to accelerate transformer training by re-using pretrained models via a learnable, linear and sparse model growth operator.[MIT News] [Paper] [Code]Signal Processing for Implicit Neural RepresentationsAdvances in Neural Information Processing Systems (NeurIPS), 2022We propose a theoretically grounded signal processing framework for Implicit Neural Representations (INR), which analytically manipulates INRs on the weight space through differential operators.[Project Page] [Paper] [Code]Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great AgainAdvances in Neural Information Processing Systems (NeurIPS), 2022We derive a topology-aware isometric initialization and a Dirichlet Energy guided achitectural rewiring technique that boost vanilla-GCNs to be the state-of-the-art.[Paper] [Code]Unified Implicit Neural StylizationEuropean Conference on Computer Vision (ECCV), 2022This work explores stylizing an implicit neural representation, using a generalized approach that can apply to various 2D and 3D representations.[Project Page] [Paper] [Code]SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single ImageEuropean Conference on Computer Vision (ECCV), 2022We present Single View NeRF (SinNeRF) consisting of thoughtfully designed semantic and geometry regularizations to train neural radiance field using only a single view.[Project Page] [Paper] [Code]Neural Implicit Dictionary Learning via Mixture-of-Expert TrainingInternational Conference on Machine Learning (ICML), 2022We present Neural Implicit Dictionary (NID) that learns and represents implicit neural representation as a sparse mixture of expert networks.[Paper] [Code]Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark StudyIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022We present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.[Paper] [Code]Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level AugmentationsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022We propose Aug-NeRF which augments NeRF with worst-case perturbations in three distinct levels with physical grounds.[Paper] [Code]CADTransformer: Panoptic Symbol Spotting Transformer for CAD DrawingsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, OralWe present CADTransformer, a transformer based framework, to tackle the panoptic symbol spotting task for computer-aided design drawings.[Paper] [Code]Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to PracticeInternational Conference on Learning Representations (ICLR), 2022We prove that self-attention is no more than low-pass filter, and propose two simple yet effective methods to counteract excessive smoothening.[Paper] [Code]Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical SystemsAdvances in Neural Information Processing Systems (NeurIPS), 2021We presents the Delayed Propagation Transformer (DePT) that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world.[Paper] [Code]SoGCN: Second-Order Graph Convolutional NetworksarXiv preprint, 2021We prove that second-order graph convolution is the maximally localized kernel with full representation power.[Paper] [Code]TightCap: 3D Human Shape Capture with Clothing Tightness FieldACM Transactions on Graphics (TOG), 2021We propose a data-driven approach to capture both the human shape and dressed garments with only a single 3D human scan, by predicting clothing tightness.[Project Page] [Paper] [Code]
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