Showing 2026Show all
3 papers · 1 filter
cs.CV2026
Laplacian Multi-scale Flow Matching for Generative Modeling
Zelin Zhao, Petr Molodyk, Haotian Xue +1
In this paper, we present Laplacian multiscale flow matching (LapFlow), a novel framework that enhances flow matching by leveraging multi-scale representations for image generative…
cs.LG2026
Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
Jaemoo Choi, Yuchen Zhu, Wei Guo +6
Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these tasks remain challenging because diffu…
cs.LG2026
Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation
Bo Yuan, Zelin Zhao, Petr Molodyk +2
Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.g., CadQuery) from natural-language prompts. In practice, however, geomet…