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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 7
Finite Spectral Depth Controls Gaussian Entrance Rank and Avoidance of the First Gradient-Flow Saddle
Matrix factorization provides a simple model for studying the training dynamics of neural networks.A Short Proof and Generalization of the Rainbow Cycle Theorem and Improved Bounds for Binary Even-Query LCCs
A \emph{rainbow cycle} in a list-edge-colored graph is a cycle where no color appears more than once.A Low-Diameter Nash-Williams--Tutte Theorem
The Nash-Williams--Tutte theorem characterizes the existence of $k$ edge-disjoint spanning trees by a condition on every vertex partition.Rank-metric intersecting codes
Intersecting codes are codes in which any two nonzero codewords share a common nonzero coordinate, and they have been extensively studied due to their applications in communication theory…Beyond Multi-Index Models: Learning Intersections of Degree-2 PTFs
We give an algorithm for PAC learning intersections of $k$ degree-2 polynomial threshold functions over $R^d$ under a Gaussian distribution.Solving the Nonlinear Westervelt Equation in Ultrasound Propagation Using Physics-Informed Neural Networks
Modeling the nonlinear acoustic wave propagation in biological tissues needs accuracy, especially in ultrasound imaging and therapeutic applications.A Random Matrix Analysis of Symmetric Tensor Unfolding
We study a fundamental problem in machine learning: partial recovery of a planted signal $\boldsymbol{v} \in \mathbb{S}^{n-1}$ from a spiked tensor $\boldsymbol{\mathsf{X}} = λ\…Path Decompositions of Sparse Graphs via Gaussian Sweeps and Faster Exponential Algorithms
Using the bisection bound of Monien and Preis, Fomin and Høie proved in 2006 that every graph of maximum degree 3 on $n$ vertices has pathwidth at most $n/6+o(n)$.Ensembles of Random Partition-Based Models under Covariate Shift
Distribution shifts, including covariate shifts, are ubiquitous in real-world applications of machine learning.Physics-informed traditional and generative Mori-Zwanzig-reduced models for multiscale systems
Model reduction via the Mori-Zwanzig (MZ) formalism is well understood for coarse-grained representations constructed from standard basis functions such as Fourier functions.Post-Inference Model Chaining: A Problem Statement and a Model-based Bootstrap for Training End-to-End Digital Twins
End-to-end digital twins can be constructed by fitting separate stage models and connecting them after inference.Beyond the Bochner Method: Optimal Spectral Gaps for Block Dynamics of Spin Systems like the $O(N)$ Model
Let $μ$ be a probability distribution on a spin system with $n$ sites and possibly infinite spin spaces.Post-stratified importance sampling
Importance sampling is a building block of modern statistical computing, but the weighted estimate is prone to high or infinite variance.Shrinkage prior for implicit generative models
Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation.Logarithmic Regret for Alternating Mirror Descent
Alternating online mirror descent (AltOMD) is a simple learning rule for zero-sum games, which improves upon simultaneous OMD numerically and theoretically.De Rham cohomological architectures for PINNs and PDEs with topological holes
We attempt to alleviate failure modes of classical physics-informed neural networks (PINNs) in scenarios of topological pathologies corresponding to punctures, holes, poles, and voids by introducing…Conformal Prediction at Performative Equilibrium: The Price of Performativity
Conformal prediction gives finite-sample coverage when calibration and test points are exchangeable, but exchangeability can fail when deployment changes the data distribution.Remap for ALE Hydrodynamics via Direct Interpolation and Constraint Optimization
This work presents an optimization-based remap approach for arbitrary Lagrangian-Eulerian (ALE) hydrodynamics that preserves sharp features and physical bounds while conserving global quantities…A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry
Efficient preconditioning plays an important role in large-scale first-order optimization.On the most informative Boolean function under noise
Let $X$ be a uniform random bit string and $Y$ be obtained by flipping each bit of $X$ independently with probability $p$.
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