Ultrametric OGP - parametric RDT \emph{symmetric} binary perceptron connection
Abstract
In [97,99,100], an fl-RDT framework is introduced to characterize \emph{statistical computational gaps} (SCGs). Studying \emph{symmetric binary perceptrons} (SBPs), [100] obtained an \emph{algorithmic} threshold estimate $α_a\approx α_c^{(7)}\approx 1.6093$ at the 7th lifting level (for $κ=1$ margin), closely approaching $1.58$ local entropy (LE) prediction [18]. In this paper, we further connect parametric RDT to overlap gap properties (OGPs), another key geometric feature of the solution spa...
Description / Details
In [97,99,100], an fl-RDT framework is introduced to characterize \emph{statistical computational gaps} (SCGs). Studying \emph{symmetric binary perceptrons} (SBPs), [100] obtained an \emph{algorithmic} threshold estimate at the 7th lifting level (for margin), closely approaching local entropy (LE) prediction [18]. In this paper, we further connect parametric RDT to overlap gap properties (OGPs), another key geometric feature of the solution space. Specifically, for any positive integer , we consider -level ultrametric OGPs (-OGPs) and rigorously upper-bound the associated constraint densities . To achieve this, we develop an analytical union-bounding program consisting of combinatorial and probabilistic components. By casting the combinatorial part as a convex problem and the probabilistic part as a nested integration, we conduct numerical evaluations and obtain that the tightest bounds at the first two levels, and , closely approach the 3rd and 4th lifting level parametric RDT estimates, and . We also observe excellent agreement across other key parameters, including overlap values and the relative sizes of ultrametric clusters. Based on these observations, we propose several conjectures linking -OGP and parametric RDT. Specifically, we conjecture that algorithmic threshold , and (with possible equality for some (maybe even all) ). Finally, we discuss the potential existence of a full isomorphism connecting all key parameters of -OGP and parametric RDT.
Source: arXiv:2604.19712v1 - http://arxiv.org/abs/2604.19712v1 PDF: https://arxiv.org/pdf/2604.19712v1 Original Link: http://arxiv.org/abs/2604.19712v1
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Apr 22, 2026
Data Science
Machine Learning
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