dorsal/arxiv
View SchemaSpectral Generative Flow Models: A Physics-Inspired Replacement for Vectorized Large Language Models
| Authors | Andrew Kiruluta |
|---|---|
| Categories | |
| ArXiv ID | 2601.08893vv2 |
| URL | https://arxiv.org/abs/2601.08893 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
We introduce Spectral Generative Flow Models (SGFMs), a physics-inspired alternative to transformer-based large language models. Instead of representing text or video as sequences of discrete tokens processed by attention, SGFMs treat generation as the evolution of a continuous field governed by constrained stochastic dynamics in a multiscale wavelet basis. This formulation replaces global attention with local operators, spectral projections, and Navier--Stokes-like transport, yielding a generative mechanism grounded in continuity, geometry, and physical structure. Our framework provides three key innovations: (i) a field-theoretic ontology in which text and video are unified as trajectories of a stochastic partial differential equation; (ii) a wavelet-domain representation that induces sparsity, scale separation, and computational efficiency; and (iii) a constrained stochastic flow that enforces stability, coherence, and uncertainty propagation. Together, these components define a generative architecture that departs fundamentally from autoregressive modeling and diffusion-based approaches. SGFMs offer a principled path toward long-range coherence, multimodal generality, and physically structured inductive bias in next-generation generative models.
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"abstract": "We introduce Spectral Generative Flow Models (SGFMs), a physics-inspired alternative to transformer-based large language models. Instead of representing text or video as sequences of discrete tokens processed by attention, SGFMs treat generation as the evolution of a continuous field governed by constrained stochastic dynamics in a multiscale wavelet basis. This formulation replaces global attention with local operators, spectral projections, and Navier--Stokes-like transport, yielding a generative mechanism grounded in continuity, geometry, and physical structure.\n Our framework provides three key innovations: (i) a field-theoretic ontology in which text and video are unified as trajectories of a stochastic partial differential equation; (ii) a wavelet-domain representation that induces sparsity, scale separation, and computational efficiency; and (iii) a constrained stochastic flow that enforces stability, coherence, and uncertainty propagation. Together, these components define a generative architecture that departs fundamentally from autoregressive modeling and diffusion-based approaches. SGFMs offer a principled path toward long-range coherence, multimodal generality, and physically structured inductive bias in next-generation generative models.",
"arxiv_id": "2601.08893",
"authors": [
"Andrew Kiruluta"
],
"categories": [
"cs.LG",
"cs.CL"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Spectral Generative Flow Models: A Physics-Inspired Replacement for Vectorized Large Language Models",
"url": "https://arxiv.org/abs/2601.08893",
"version": "v2"
},
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