Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 39 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 12 tok/s Pro
GPT-5 High 18 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 191 tok/s Pro
GPT OSS 120B 456 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

A one-dimensional coagulation-fragmentation process with a dynamical phase transition (1107.3227v2)

Published 16 Jul 2011 in math.PR and cond-mat.stat-mech

Abstract: We introduce a reversible Markovian coagulation-fragmentation process on the set of partitions of ${1,\ldots,L}$ into disjoint intervals. Each interval can either split or merge with one of its two neighbors. The invariant measure can be seen as the Gibbs measure for a homogeneous pinning model \cite{cf:GBbook}. Depending on a parameter $\lambda$, the typical configuration can be either dominated by a single big interval (delocalized phase), or be composed of many intervals of order $1$ (localized phase), or the interval length can have a power law distribution (critical regime). In the three cases, the time required to approach equilibrium (in total variation) scales very differently with $L$. In the localized phase, when the initial condition is a single interval of size $L$, the equilibration mechanism is due to the propagation of two "fragmentation fronts" which start from the two boundaries and proceed by power-law jumps.

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube