AI RESEARCH
HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization
arXiv CS.LG
•
ArXi:2511.08425v3 Announce Type: replace Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distributions and enabling flexible guidance at inference time. Many downstream applications, however, demand enforcing hard constraints on generated samples (for example, robot trajectories must avoid obstacles), a requirement that goes beyond simple guidance.