Authors: Utkarsh A Mishra, David He, Yongxin Chen, Danfei Xu
Affiliation: Georgia Institute of Technology
arXiv: https://arxiv.org/abs/2601.00126
Generative models have emerged as powerful tools for planning, with compositional approaches offering particular promise for modeling long-horizon task distributions by composing together local, modular generative models. This compositional paradigm spans diverse domains, from multi-step manipulation planning to panoramic image synthesis to long video generation. However, compositional generative models face a critical challenge: when local distributions are multimodal, existing composition methods average incompatible modes, producing plans that are neither locally feasible nor globally coherent. We propose Compositional Diffusion with Guided Search (CDGS), which addresses this mode averaging problem by embedding search directly within the diffusion denoising process. Our method explores diverse combinations of local modes through population-based sampling, enforces global consistency through iterative resampling between overlapping segments, and prunes infeasible candidates using likelihood-based filtering. CDGS matches oracle performance on seven robot manipulation tasks, outperforming baselines that lack compositionality or require long-horizon training data. The approach generalizes across domains, enabling coherent text-guided panoramic images and long videos through effective local-to-global message passing.
If you use this code, please cite the following:
@inproceedings{
mishra2026compositional,
title={Compositional Diffusion with Guided search for Long-Horizon Planning},
author={Utkarsh Aashu Mishra and David He and Yongxin Chen and Danfei Xu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=b8avf4F2hn}
}