32 citations · 157 across the 45 of their papers we have counts for
8 papers · 1 filter
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning
Peihao Wang, Zhangyang Wang
We develop a theoretical framework that explains how discrete symbolic structures can emerge naturally from continuous neural network training dynamics. By lifting neural parameter…
FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting
Hengyu Liu, Yuehao Wang, Chenxin Li +6
3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands…
Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
Haoyu Wang, Peihao Wang, Mufei Li +4
Modern large language models (LLMs) are inherently auto-regressive, requiring input to be serialized into flat sequences regardless of their structural dependencies. This serializa…
CryoFastAR: Fast Cryo-EM Ab Initio Reconstruction Made Easy
Jiakai Zhang, Shouchen Zhou, Haizhao Dai +5
Pose estimation from unordered images is fundamental for 3D reconstruction, robotics, and scientific imaging. Recent geometric foundation models, such as DUSt3R, enable end-to-end…
Steepest Descent Density Control for Compact 3D Gaussian Splatting
Peihao Wang, Yuehao Wang, Dilin Wang +8
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3D…
Can Test-Time Scaling Improve World Foundation Model?
Wenyan Cong, Hanqing Zhu, Peihao Wang +7
World foundation models, which simulate the physical world by predicting future states from current observations and inputs, have become central to many applications in physical in…
Rethinking Addressing in Language Models via Contexualized Equivariant Positional Encoding
Jiajun Zhu, Peihao Wang, Ruisi Cai +3
Transformers rely on both content-based and position-based addressing mechanisms to make predictions, but existing positional encoding techniques often diminish the effectiveness o…
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing
Peihao Wang, Ruisi Cai, Yuehao Wang +4
Structured State Space Models (SSMs) have emerged as alternatives to transformers. While SSMs are often regarded as effective in capturing long-sequence dependencies, we rigorously…