A note on the dynamics of extended-context disordered kinetic spin models

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Zavatone-Veth, Jacob A., Pehlevan, Cengiz
Format: Preprint
Published: 2025
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917149962928128
author Zavatone-Veth, Jacob A.
Pehlevan, Cengiz
author_facet Zavatone-Veth, Jacob A.
Pehlevan, Cengiz
contents Inspired by striking advances in language modeling, there has recently been much interest in developing autogressive sequence models that are amenable to analytical study. In this short note, we consider extensions of simple disordered kinetic glass models from statistical physics. These models have tunable correlations, are easy to sample, and can be solved exactly when the state space dimension is large. In particular, we give an expository derivation of the dynamical mean field theories that describe their asymptotic statistics. We therefore propose that they constitute an interesting set of toy models for autoregressive sequence generation, in which one might study learning dynamics.
format Preprint
id arxiv_https___arxiv_org_abs_2507_18461
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A note on the dynamics of extended-context disordered kinetic spin models
Zavatone-Veth, Jacob A.
Pehlevan, Cengiz
Disordered Systems and Neural Networks
Inspired by striking advances in language modeling, there has recently been much interest in developing autogressive sequence models that are amenable to analytical study. In this short note, we consider extensions of simple disordered kinetic glass models from statistical physics. These models have tunable correlations, are easy to sample, and can be solved exactly when the state space dimension is large. In particular, we give an expository derivation of the dynamical mean field theories that describe their asymptotic statistics. We therefore propose that they constitute an interesting set of toy models for autoregressive sequence generation, in which one might study learning dynamics.
title A note on the dynamics of extended-context disordered kinetic spin models
topic Disordered Systems and Neural Networks
url https://arxiv.org/abs/2507.18461