AegisFlow: Training-Free Non-Myopic Path-Safe Guided Flow Matching
arXiv:2610.39825v1 Announce Type: new Abstract: Pretrained generative models, such as diffusion and flow matching models, have demonstrated exceptional capabilities in synthesizing high-quality data. However, steering their generative trajectories to satisfy strict constraints--such as identity preservation in image editing or obstacle avoidance in robotics--remains fundamentally challenging. While recent training-free guidance methods enforce path-safety, they rely heavily on min-norm controll…