{"slug": "stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting", "title": "Stochastic Dynamics of Generative AI: From Autoregressive Models to Interacting LLM Networks and Neural SDEs", "summary": "A new arXiv paper, 2611.07065v1, frames three classes of generative AI models as stochastic dynamical systems: large language models as high-dimensional nonlinear autoregressive models with attention-based dependencies, networks of interacting LLMs characterized through mean-field ordinary differential equations, and neural stochastic differential equations for continuous-time generative modeling of stochastic trajectories, including data observed at irregular intervals. The paper links pretraining by conditional maximum likelihood to sequential token generation, uses the mean-field framework to describe emergent preferential attachment and persistent structural biases such as type-dependent glass-ceiling effects, and proposes Wiener-space cubature methods based on rough-path analysis as an efficient alternative to Monte Carlo estimation for training neural SDEs.", "body_md": "arXiv:2611.07065v1 Announce Type: new \nAbstract: This paper develops a stochastic dynamical-systems perspective on three classes of generative AI models. First, we formulate large language models (LLMs) as high-dimensional nonlinear autoregressive models with attention-based dependencies. This formulation connects pretraining by conditional maximum likelihood to sequential token generation and provides a probabilistic interpretation of LLM operation. Second, we consider networks of interacting LLMs and characterize their collective behavior through mean-field ordinary differential equations. These dynamics provide a framework for understanding emergent preferential attachment and persistent structural biases, including type-dependent glass-ceiling effects. Third, we consider neural stochastic differential equations (SDEs) as continuous-time generative models for stochastic trajectories, including those observed at irregular time intervals. We discuss their training with particular emphasis on Wiener-space cubature methods based on rough-path analysis as an efficient alternative to Monte Carlo estimation. Together, the three settings address probabilistic prediction, information exchange and emergent behavior in interacting networks, and path-space generative modeling.", "url": "https://wpnews.pro/news/stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting", "canonical_source": "https://arxiv.org/abs/2611.07065", "published_at": "2026-11-06 05:00:00+00:00", "updated_at": "2026-11-06 05:20:19.175979+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "generative-ai"], "entities": ["arXiv", "LLMs", "neural stochastic differential equations"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting", "markdown": "https://wpnews.pro/news/stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting.md", "text": "https://wpnews.pro/news/stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting.txt", "jsonld": "https://wpnews.pro/news/stochastic-dynamics-of-generative-ai-from-autoregressive-models-to-interacting.jsonld"}}