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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 38
BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous…Improved rapid phase ordering for Ising dynamics on random regular graphs
We study Glauber dynamics for the Ising model on random $d$-regular graphs with $n$ vertices at low temperature, in the regime $β\geq C/d$ where $C,d$ are sufficiently large constants.Anytime-Valid Runtime Monitoring of Learned Barrier Certificates
Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ…Practical Advantages of Online Conformal Prediction for Data Streams
Full conformal prediction offers finite-sample marginal coverage guarantees under exchangeability, but exact computation can be costly, while its classical guarantees do not directly extend to…Constant Step Sizes for Muon-Type Spectral Optimizers on Strongly Convex Quadratics
Muon and related optimizers update a weight matrix with a spectrally transformed gradient.Computing excited states of dipolar Bose--Einstein condensates by constrained gentlest ascent dynamics
Dipolar Bose--Einstein condensates (BECs), i.e., BECs with long-range dipole-dipole interactions (DDI), exhibit extraordinarily rich physics and serve as a versatile experimental platform for…Automatic Differentiation with Constraints in Forward and Inverse Problems
Approximate solutions of partial differential equations (PDEs) on unstructured grids can be represented on a graph.The Fer expansion of a driven spin under $\mathfrak{su}(2)$ operations cannot terminate beyond first order
The Fer expansion writes the propagator of a driven quantum system as an ordered product of exponentials, each generated by the time integral of an effective Hamiltonian that governs the evolution…Annealed correlation and quenched noise sensitivity in biased deep neural networks
In earlier work by Jonasson, Steif and Zetterqvist (2023), fully connected deep neural networks with layers of width $n$, i.i.d.\ standard Gaussian weights, sign activations and no bias terms were…When Momentum Helps Polyak--Ruppert Averaging
Polyak--Ruppert averaging gives SGD and momentum methods the same asymptotic covariance.Optimal aggregation in multiclass classification
This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification.Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health.Weak-$L^α$ Noise, Strong Guarantees: Gradient Clipping under Affine Heavy Tails
Gradient clipping is a key tool for obtaining high-probability convergence guarantees for stochastic first-order methods under heavy-tailed noise.Norm-optimal model order reduction in reproducing kernel Hilbert spaces
We consider the problem of model order reduction (MOR) in reproducing kernel Hilbert spaces (RKHSs).Local Work, Global Progress: A Convergence Theory of AI Agent Collaboration
Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirically, without convergence guarantees.Learning Normal Coordinates on Riemannian Manifolds with Flow Charts
Riemannian manifolds are powerful models for problems with non-Euclidean structure.Confessions of a Masked MDP
In reinforcement learning, one presumes the action set to be known ahead of time, and, thus, one must know if any state-action pairs are not permissible ahead of computations.Do Neural Models Prefer Their Submodels?
A neural model is the set of functions that can be expressed by a given neural network architecture.Geometry-Agnostic Data-Driven Discovery of Extreme Scenarios
We introduce a two-stage data-driven neural framework for discovering dynamically admissible extreme scenarios.Variance Reduction with Delayed Feedback for Non-Convex Optimization
We study variance reduction for non-convex optimization when gradient evaluations return with a delay.
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