arXiv:2602.18695v2 Announce Type: replace Abstract: Existing insertion-based masked diffusion models that generate sequences by interleaving token insertion with unmasking use fixed schedules that are not dependent on the data. For structured sequences like graphs and molecules, learning data-dependent generation orders can improve generation quality by reducing uncertainty over the action space. We propose LoFlexMDM, an insertion-based masked diffusion model with learnable order dynamics that learns data-dependent insertion and unmasking rates. We generalize the discrete flow matching framewo
The continuous drive for more efficient and robust generative AI models pushes research into optimizing generation processes, especially for complex structured data like graphs and molecules.
Improving the generation quality of complex structured sequences through learnable order dynamics could lead to more effective design and discovery in fields like materials science and drug development.
The development proposes a method for AI models to learn data-dependent generation orders, moving beyond fixed schedules and potentially enhancing the fidelity and utility of generated structured data significantly.
- · AI researchers
- · Drug discovery companies
- · Materials science
- · Generative AI platforms
- · Generative models relying on fixed schedules
More sophisticated generative AI models for structured data like molecules and graphs.
Accelerated design and discovery cycles in chemistry, biology, and materials science due to higher quality generated data.
Potential for new breakthroughs in science and engineering by enabling AI to autonomously design novel structures with desired properties.
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Read at arXiv cs.LG