5 papers · 1 filter
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Yixian Xu, Shengjie Luo, Liwei Wang +2
Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting qual…
In-Place Test-Time Training
Guhao Feng, Shengjie Luo, Kai Hua +4
The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new informat…
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs
Guhao Feng, Kai Yang, Yuntian Gu +6
Despite the remarkable success of Transformer-based large language models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a signi…
Bridging Geometric States via Geometric Diffusion Bridge
Shengjie Luo, Yixian Xu, Di He +3
The accurate prediction of geometric state evolution in complex systems is critical for advancing scientific domains such as quantum chemistry and material modeling. Traditional ex…
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He +3
Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is govern…