We are excited to share that a paper from CLeAR has been accepted to ICML 2026! Here’s a snapshot:
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards. Led by Dr. Xuehui Yu, this work asks: how can we harness large, complex pretrained generative priors to satisfy multiple constraints at inference time without drifting off the data manifold (i.e., avoiding hallucinated generation)? The paper introduces Conflict-Aware Additive (CAR) Guidance, a plug-and-play module that detects and rectifies off-manifold drift on the fly. CAR Guidance is validated across pixel-space image editing, robot planning, and 3D point-cloud robot manipulation. The paper is available here, and the code is available here.
