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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: 971 arXiv preprints and 29 OpenAlex records.
Newest papers, page 24
The Power Watershed as a Differentiable Segmentation Layer
Can a Watershed transform, the classical tool of morphological segmentation, be placed at the end of a deep network and trained through?Estimating the number of nodes and paths in a DAG by following a random path
We investigate a simple method to compute an unbiased estimate of the number of nodes of a directed acyclic graph (DAG) by following a random directed path.Goldbach Representations in Quantum Optics and Metrology
We derive a relation between Goldbach representations and photon-number statistics in a quantum-optical setup.Spontaneous Spirals in Spatial Systems
Several chemical and biological systems produce spontaneous spirals in their dynamical behavior, such as the Belousov-Zhabotinsky reaction in chemistry and the slime mold Dictyostelium in biology.A Primer on Instanton Physics - A Short Course
These six lectures, delivered at the University of New Brunswick 6-21 October 2026, are an introduction to instantons in quantum mechanics and quantum field theory for senior undergraduate and…Integrating multi-source local information for global undirected graph reconstruction via integer programming
Graphical models are widely used to represent conditional independence structures, but learning them is challenging when data are fragmented across sources that measure different subsets of…Metropolized backward sampling for conditional sequential Monte Carlo
Particle Gibbs samplers with backward sampling are widely used for sampling the smoothing distribution of state-space models, but, with few particles, the new trajectory often coincides with the…Rennala NSGD: Asynchronous Stochastic Optimization Beyond Euclidean Geometry
The empirical success of matrix-aware optimizers, such as Muon (Jordan et al., 2024) and Shampoo (arXiv:1802.09568), highlights the potential of non-Euclidean geometry for deep learning, including…Pinpointing Pivot Points: The DECIDE Framework for Data Analysis Decision Importances
Human judgment calls made throughout the data science life cycle can change the conclusions drawn from data.From Entanglement Bootstrap to DHR Theory
We establish a direct connection between the entanglement bootstrap and the Doplicher--Haag--Roberts theory of superselection sectors for two-dimensional quantum spin systems.The geometry of Floquet states
This paper studies the moduli space of "good" periodic Hamiltonians, i.e., periodic Hamiltonians with nondegenerate Floquet spectrum.Dual Area-Volume-of-Fluid (DAVOF): co-advecting volume and face-area fractions for a closed-form, cell-local interface normal and position on unstructured meshes
Geometric Volume-of-Fluid (VOF) on unstructured polyhedral meshes reconstructs the interface orientation from the volume fractions of a stencil of neighbours; on irregular cells and in the presence…Memorization Capacity of Invariant Convolutional Neural Nets
Neural networks are powerful memorizers which are often able to interpolate their training data even when the labels have been randomized.Optimization-based lane-changing trajectory planning in mixed traffic with embedded input convex neural networks
Lane-changing (LC) maneuvers are highly challenging vehicle interactions, with substantial implications for traffic safety and efficiency.Orthogonal Inference for Conditional Z-Estimation without Cross-Fitting: A Distributional Nearest-Neighbor Approach
Estimating conditional parameters, such as heterogeneous treatment effects, often requires rich covariate adjustment, even when the parameters are reported along only a few dimensions.A Spectral Boundary for Independence Learnability
Learning independence is fundamental to statistical inference and machine learning, yet learning independence between entire time series by a test remains challenging when only a single observation…Tunable Spatial Task Allocation with Application to Maritime Swarms
In this chapter, we explore how a team of autonomous agents can flexibly reconfigure the distribution of its members across many spatially embedded tasks using a task allocation strategy grounded in…Exponential-Nemytskii Neural Operators: Learning Field Evolution Across Step Sizes
In variable-step operator learning, agreement at observed step sizes does not by itself determine predictions at other temporal resolutions.Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting
Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime.Tabular foundation models are not a shortcut to calibrated clinical risk prediction: a pre-registered simulation against the true risk
Tabular foundation models are pretrained networks that predict risk from a table without fitting a model.
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