Title: IAGA: Identity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Gaussian approximation, data identity, efficient generation, 3D molecular generation

Abstract: Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named IAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of GPPGMs. Our key insight is that different data modalities converge to Gaussianity at markedly different rates during the forward process. Based on this observation, we analytically identify a characteristic step at which molecular data attains sufficient Gaussianity, after which the trajectory can be replaced by a closed-form Gaussian approximation. Unlike existing accelerators that coarsen or reformulate trajectories, our approach preserves full-resolution learning dynamics while avoiding redundant transport through distributional states with little data identity. Experiments on 3D molecular generation benchmarks demonstrate that our IAGA achieves substantial improvement on both generation quality and computational efficiency.