Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modeling
arXiv:2603.06615v1 Announce Type: new Abstract: For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden and data imbalance. To this end, we propose an Annealed Co-Generation (ACG) framework that replaces high-dimensional diffusion modeling with a low-dimensional diffusion model, which enables multivariate co-generation by composing pairwise variable generations. We first train an unconditional diffusion model over causal variables that are disentangled into pairs. At inference time, we recover the joint distribution by coupling these pairwise models through shared common variables, enabling coherent multivariate generation without any additional training. By employing a three-stage annealing process-Consensus, Heating, and Cooling-our method enforces consistency across shared common variables and progressively constrains each pairwise data distribution to lie on a
arXiv:2603.06615v1 Announce Type: new Abstract: For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden and data imbalance. To this end, we propose an Annealed Co-Generation (ACG) framework that replaces high-dimensional diffusion modeling with a low-dimensional diffusion model, which enables multivariate co-generation by composing pairwise variable generations. We first train an unconditional diffusion model over causal variables that are disentangled into pairs. At inference time, we recover the joint distribution by coupling these pairwise models through shared common variables, enabling coherent multivariate generation without any additional training. By employing a three-stage annealing process-Consensus, Heating, and Cooling-our method enforces consistency across shared common variables and progressively constrains each pairwise data distribution to lie on a learnable manifold, while maintaining high likelihood within each pair. We demonstrate the framework's flexibility and efficacy on two distinct scientific tasks: flow-field completion and antibody generation. All datasets and code will be made publicly available upon publication.
Executive Summary
This article proposes an Annealed Co-Generation (ACG) framework for multivariate co-generation in scientific applications. ACG replaces high-dimensional diffusion modeling with a low-dimensional diffusion model, enabling pairwise variable generations. The framework employs a three-stage annealing process to enforce consistency across shared common variables and progressively constrain each pairwise data distribution. The authors demonstrate ACG's flexibility and efficacy on flow-field completion and antibody generation tasks. While ACG offers improvements over joint modeling, its performance may be sensitive to the choice of pairwise variables. Furthermore, the computational efficiency gains may be offset by the increased complexity of the three-stage annealing process. Overall, ACG presents a promising approach for addressing the challenges of multivariate co-generation.
Key Points
- ▸ ACG replaces high-dimensional diffusion modeling with a low-dimensional diffusion model
- ▸ The framework employs a three-stage annealing process for consistency and progressive constraint
- ▸ ACG demonstrates improved flexibility and efficacy on scientific tasks
Merits
Improved Computational Efficiency
ACG reduces the computational burden associated with high-dimensional diffusion modeling
Enhanced Data Balancing
The framework mitigates data imbalance by composing pairwise variable generations
Flexibility and Efficacy
ACG demonstrates improved performance on diverse scientific tasks
Demerits
Sensitivity to Pairwise Variable Choice
The performance of ACG may be highly dependent on the selection of pairwise variables
Increased Complexity of Annealing Process
The three-stage annealing process may introduce additional computational overhead
Potential for Overfitting
The framework's reliance on shared common variables may lead to overfitting
Expert Commentary
The ACG framework represents a significant advancement in the field of multivariate co-generation. By leveraging the strengths of pairwise variable generation, ACG offers improved computational efficiency and enhanced data balancing. However, the framework's sensitivity to pairwise variable choice and increased complexity of the annealing process necessitate careful consideration. Furthermore, the potential for overfitting highlights the need for rigorous testing and validation. As the field continues to evolve, ACG is likely to play a prominent role in addressing the challenges of multivariate co-generation. Nevertheless, its adoption will depend on addressing the identified limitations and ensuring its robustness in practice.
Recommendations
- ✓ Careful selection of pairwise variables to minimize sensitivity to choice
- ✓ Implementation of additional regularization techniques to mitigate overfitting