arXiv:2511.05924v3 Announce Type: replace Abstract: Estimating probability density and its score from samples remains a core problem in generative modeling, Bayesian inference, and kinetic theory. Existing methods are bifurcated: classical kernel density estimators (KDE) generalize across distributions but suffer from the curse of dimensionality, while modern neural score models achieve high precision but require retraining for every target distribution. We introduce DiScoFormer (Density and Score Transformer), a ``train-once, infer-anywhere" equivariant Transformer that maps i.i.d. samples to
The continuous evolution of generative AI and probabilistic modeling demands more efficient and adaptable methods for density and score estimation.
This breakthrough offers a 'train-once, infer-anywhere' solution, significantly reducing computational overhead and accelerating AI development across various applications.
Current methods requiring retraining for each distribution become less efficient, potentially standardizing and decentralizing certain aspects of AI model deployment and adaptation.
- · AI researchers and developers
- · Generative AI companies
- · Industries relying on Bayesian inference
- · Developers of distribution-specific neural models
- · Companies heavily invested in specialized, non-generalizable AI training
AI model development becomes faster and less resource-intensive due to a novel plug-in density and score estimation method.
This generalization capability could lead to more robust and adaptable AI agents and systems that can function in diverse, unseen environments without extensive retraining.
The reduced need for specialized training data could democratize advanced AI applications, fostering innovation beyond well-resourced labs.
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Read at arXiv cs.LG