arXiv:2512.08280v3 Announce Type: replace-cross Abstract: Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability for control. We propose Model Predictive Diffuser (MPDiffuser), a compositional diffusion framework that combines a diffusion planner with a dynamics diffusion model to generate task-aligned and dynamically plausible trajectories. MPDiffuser interleaves planner and dynamics updates during sampling, progressively correcting feasibility while preserving task intent. A lightweight ranking module then
The continuous evolution of AI models, particularly diffusion models, alongside the increasing demand for robust autonomous decision-making in real-world applications, drives the need for more reliable control methods.
This development enhances the reliability and practicality of AI agents and autonomous systems by addressing a key limitation in current diffusion models, making their outputs more aligned with real-world physics and tasks.
The ability to generate dynamically plausible and task-aligned trajectories via diffusion models means a significant step towards more dependable and deployable autonomous decision-making in complex environments.
- · AI agents developers
- · Robotics industry
- · Logistics and automation sector
- · Manufacturers of autonomous systems
- · Companies with less sophisticated control algorithms
- · Systems highly reliant on human oversight
- · Inefficient simulation-based training methods
Improved performance and broader adoption of AI agents and autonomous robotic systems.
Accelerated development of complex autonomous applications in logistics, manufacturing, and defense.
Reduced operational costs and increased efficiency across various industries as autonomous systems become more reliable.
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Read at arXiv cs.AI