A field on ReadSearch
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 32
EnerGNN: Learning Optimization-Compatible Energy Functions for Exact Constrained Combinatorial Inference
Learning-based approaches to combinatorial optimization often predict solutions directly or use predictions to restrict search, while expressive energy-based models typically rely on approximate…Searching for binary orientable sequences
An aperiodic binary orientable sequence is a finite sequence of zeros and ones for which, for some integer $n$, all the subsequences of $n$ consecutive symbols, read either forwards or backwards…Additive uncertainty relations via pseudospectral methods
Given Hermitian observable $A$, the variance of $A$ is given by the difference of expectations $\langle A^2\rangle - \langle A\rangle^2$, where the expectation of an observable implicitly assumes a…Can Spontaneous Time-Translation Symmetry Breaking Be Quantized Without Explicit Branches?
Can classically branched dynamics associated with spontaneous time-translation-symmetry breaking be quantized without explicitly retaining the branches of the Hamiltonian?A Dynamic Inventory Rationing Model for Last-Mile Delivery of Medication in Liberia's Community Health Program
We study the dynamic allocation of scarce medications in Liberia's community health program.Implicit Neural Representations for Variational Problems on Graphons
We propose a neural exploratory framework for variational problems over graphons, i.e., symmetric measurable functions $[0,1]^2 \to [0,1]$, that arise as limits of dense graph sequences.Covariance-Informed Ensemble Score Filtering: Embedding Localized Covariance Geometry into Score-Based Transport
error covariance models are central to atmospheric data assimilation because they spread partial observational information across space, variables, and dynamically coupled scales.Solving and Learning Navier-Stokes Dynamics with the Neural Basis Method
Physics-informed machine learning has become a major direction in predictive modeling of fluid dynamics, combining governing physics with learned representations.Performing a Quantum Subroutine Using Cluster States
Quantum subroutines, like their classical counterparts, serve as essential components of larger algorithms.Empirically Calibrated Differential Games for Simulating Driver Interactions
Game-theoretic models can resolve vehicle interactions without hand-written priority rules, but whether they describe human drivers once calibrated has rarely been tested.CoPiT: A conditional Position-induced Transformer for aerodynamic field prediction on unstructured meshes
High-fidelity Reynolds-averaged Navier--Stokes (RANS) simulations remain a standard tool for aerodynamic analysis, yet each solve on an unstructured mesh is computationally expensive, making…On the Limits of Stochastic Push-Pull over Time-Varying Digraphs
Stochastic Push-Pull achieves linear speedup with respect to the network size $n$ on static strongly connected digraphs, but whether this speedup persists on time-varying networks remains unclear.Sharpness-aware Orthogonalized Momentum Algorithms
Adversarial sharpness penalty per iteration of a base optimizer, known as sharpness-aware minimization (SAM), is an accepted technique to improve generalization in deep learning.L0-Norm-Based CycleGAN for Unpaired Brain CT-to-MR Synthesis
Computed tomography (CT) and magnetic resonance (MR) imaging provide complementary information for the brain, but acquiring both modalities is not always possible.Random Jump Forests
Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models.Exact Global Minimax Risk near Nonidentification
We study squared-error estimation when a nuisance parameter is unidentified on a submodel but affects a target that vanishes there.Contextual Decision Making: Predictive or Generative
Contextual decision making uses observed covariates to guide decisions under uncertainty.Universal neural network approximation of symmetric polyconvex energies and envelopes
Symmetric polyconvexity provides a sufficient condition for weak lower semicontinuity in small-strain elasticity and a tractable lower bound for the relaxation of non-(quasi)convex energies.Acquisition for Spherical-Linear Bayesian Optimization on the Cube
Recent work demonstrated that executing Bayesian Optimization (BO) using simply a linear surrogate with a clever transformation is enough to approximate the performance of the standard…An Invitation to Algebraic Methods in Quantum Theory
In this paper, we present various well-studied examples of quantum systems, and discuss how they might be analysed algebraically.
Get the few papers worth your time in Mathematics & Statistics every morning.
Choose your topics and ReadSearch reads the new papers in them each day, then brings you the few worth reading, each with its key finding in a sentence.