arXiv:2602.16169v2 Announce Type: replace Abstract: Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on discrete sequence generation. We argue that the bottleneck is not continuity itself, but a representation in which denoising depends on timestep-indexed noise regimes. We introduce \emph{Discrete Stochastic Localization} (DSL), a continuous-state framework with unit-sphere token embeddings whose Bayes-optimal denoiser is invariant to the nominal signal-to-noise ratio (SNR) under the localizat
This research addresses fundamental limitations in current non-autoregressive generation models, particularly in efficiently handling discrete sequences, which is a major area of active AI development.
Improved discrete sequence generation could significantly enhance the performance and efficiency of AI models in fields like natural language processing and protein design, leading to faster inference and training.
The introduction of Discrete Stochastic Localization (DSL) offers a new continuous-state framework that aims to overcome the performance gap between continuous and discrete diffusion models, potentially accelerating AI model development.
- · AI researchers
- · Natural Language Processing (NLP) sector
- · Biotech (protein design) sector
- · GPU manufacturers
- · Inefficient masked discrete diffusion models
- · Companies relying on slower generation methods
Non-autoregressive generation models become more performant and efficient for discrete data.
Faster and more accurate AI model training and inference for sequence-based tasks.
Accelerated development of AI agents capable of complex discrete reasoning and code generation, relying on highly efficient sequence processing.
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Read at arXiv cs.LG