Evidence map›Paper›PMID 41583832›Full record

ArticleJournal of physics. A, Mathematical and theoretical2026

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

Jacob A Zavatone-Veth, Cengiz Pehlevan

Abstract read
In one paragraph

Article in Journal of physics. A, Mathematical and theoretical, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jacob A Zavatone-VethSociety of Fellows and Center for Brain Science, Harvard University, Cambridge, MA, United States of America.ORCID https://orcid.org/0000-0002-4060-1738
Cengiz PehlevanJohn A. Paulson School of Engineering and Applied Sciences, Kempner Institute for the Study of Natural and Artificial Intelligence, and Center for Brain Science, Harvard University, Cambridge, MA, United States of America.ORCID https://orcid.org/0000-0001-9767-6063

Funding

Understanding data-driven models for neural dynamicsDP5OD037354 · OD · HARVARD UNIVERSITY · PI Jacob Andreas Zavatone-Veth · 2024 to 2026
$1.3M
NIH HHS DP5 OD037354
6 · The paper itself

Abstract

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.

Indexed as

dynamical mean field theorykinetic Ising modelsequence modeling

Identifiers

PMID41583832
PMCPMC12828263

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.