arXiv:2605.05629v3 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $\mathbb S^{d-1}$. There the von Mises-Fisher (vMF) distribution induces a natural noise process and admits a closed-form conditional score. The conditional velocity is in general intractable. Exploiting the radial symmetry of the vMF density we reduce the continuity equation on $\mathbb S^{d-1}$ to a scalar OD
This research addresses a fundamental problem in generative AI (modeling discrete sequences) with a novel approach (spherical geometry) that could lead to more robust and efficient models in the near future.
Advanced techniques for sampling categorical data are crucial for improving the performance and efficiency of large language models and other AI systems dealing with discrete sequence generation.
This research potentially changes the mathematical foundations for how certain AI models handle discrete data, offering an alternative to Euclidean space or probability simplex methods for generative tasks.
- · AI researchers
- · Generative AI developers
- · NLP researchers
Improved efficiency and accuracy in generative models, particularly for text generation or other discrete sequences.
New architectures or training paradigms emerging in AI that leverage spherical embeddings for discrete data.
Reduced compute requirements for certain AI tasks, potentially broadening access to advanced generative capabilities.
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Read at arXiv cs.CL