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Mathematics & Statistics
New mathematics and statistics is posted on arXiv, often long before it appears in a journal. This page shows the newest papers ReadSearch has read, from algebra and geometry to analysis, probability and applied mathematics.
The newest 1,000 papers, from 13 October 2026 to 5 November 2026: 968 arXiv preprints and 32 OpenAlex records.
Newest papers, page 36
Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming
Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers.Adapted Fröhlich-Pizzo Model for Horizon-Induced Decoherence
The Fröhlich-Pizzo (FP) model is a quantum-mechanical model originally developed to describe the interaction between an atom and the electromagnetic field in the nonrelativistic limit $c\to\infty$.The Lévy wave packet and fractional uncertainty relations
This work revisits the debate on fractional uncertainty relations that arose a decade ago and tries to settle it.Alternative Strategies for Active Learning with Bayesian Bradley Terry Model
Comparative judgement uses pairwise comparisons to rank items, but recovering accurate rankings from a limited number of judgements depends on both the inference method and the pairs selected.Density-Level Backward Error for Low-NFE Generative Flows
Low-NFE generative ODE sampling is constrained not only by solver order, but also by how a fixed budget of network evaluations is distributed along the flow.An Exact 93-Move Solution to a Nonlocal Crystal-Exchange Puzzle
We determine the minimum number of directional commands needed to solve alevel of Order of the Sinking Star, modeled as a crystal-exchangepuzzle on an 11*11 board.Asynchronous, Generation-Free ABC: Streaming AMIS Reweighting for Heterogeneous Simulator Workloads on HPC
Approximate Bayesian computation (ABC) enables Bayesian inference for models with intractable likelihoods by estimating parameter posterior distributions from simulated data.A unified approach for estimation of unnormalized models via Stein operators
In this paper, we develop a unified framework connecting proper scoring rules and Stein's method for the estimation of unnormalized models.Geometric knowledge spaces, Attention pairing and Architectures
In this paper, we propose a geometric approach to deriving attention mechanisms and exploring potential new transformer architectures.Improved Training of Strong Stochastic Flow Maps
Stochastic flow maps promise few-step sampling of stochastic differential equations (SDEs) by learning to directly jump between times along the solution path, yet training these models to learn this…Lifting PCA in the Wasserstein Space
Principal Component Analysis (PCA) is a fundamental tool for statistical dimension reduction, yet no canonical extension to distribution-valued data has emerged within the framework of optimal…Quantum f-divergences from retrodictive tests: a unifying classical variational principle
The same classical f-divergence admits inequivalent extensions to quantum states.Quantum Charge as a Particle/Antiparticle Label
We show that the concept of particles and antiparticles is related to the Weyl-Heisenberg group $W\!H_3$.Asymmetric Likelihood-Free Hypothesis Testing
In likelihood-free hypothesis testing (LFHT), given samples from two unknown distributions $P,Q$ on $[n]$ with $\mathrm{d_{TV}}(P,Q)\geq\varepsilon$, and from a third distribution $D\in\{P,Q\}$, the…Tractable Inference with Kolmogorov-Arnold Networks
Machine-learning surrogates in computational science often require not only accurate predictions but also expectations, variances, conserved integrals, and normalization constants.Preventing Model Extraction in Contextual Linear Optimization
Consider a contextual linear optimization problem, where a predict-then-optimize (PTO) system returns optimal decisions without revealing the cost vectors used in its downstream linear program.Existence of Minimizers for the Gaussian Mumford-Shah Model via Covariance Floors on the Value Distribution
The Gaussian Mumford-Shah model segments an image into regions by minimizing a continuous multivariate Gaussian negative log-likelihood term plus a perimeter penalty.Finite Classification of Transversal Diagonal Gates Beyond Stabilizer Codes
We establish that transversal diagonal gates on exact qubit codes of distance at least two can be exhaustively classified within a finite search space.Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning
Accuracy alone gives little information about which features or model components improve probabilistic fit, which outcomes benefit, or how fit changes during training.Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined.
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