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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 6
Aggregate Calibration Can Mask Stress-State Undercoverage: Bounded-Memory Forecast Intervals under Recurrent Financial Stress
Sequential forecast intervals are often evaluated by aggregate coverage even when calibration memory is bounded.Integrating Machine Learning and Uncertainty Quantification for Real-Time Decision-Making in Digital Twins: A System-Centric Review and Perspective
Digital Twins are evolving from passive virtual representations into closed-loop systems that continuously synchronize with physical assets and drive real-time decisions.Testing Implies Distance Estimation with Polynomial Loss
A tester decides whether a graph has a property or is far from having it.An example of universal braiding in Links--Gould topological quantum computation
We construct a universal quantum computational encoding via braiding in Links--Gould fusion spaces associated to $U_q^H(\mathfrak{sl}(2|1))$ where $q$ is on the unit circle and is not a root of unity.How Risky Is Your Data-Driven Policy?
Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing their \emph{average} performance.State Evolution and Universality of Iterated Linear Programs for the Binary Perceptron
The binary perceptron is a random constraint satisfaction problem that asks for a sign vector satisfying a system of random linear inequalities.Approximate Synthetic Spectra of Quantum Systems Associated with Compact Lie Groups
How can noncommuting quantum observables retain the geometry of a classical phase space?Computing Invariant Reachable Sets through Sum-of-Squares Programming
Funnels, time-varying invariant sets around nominal trajectories, certify that a closed-loop system tracks its plan, and can be composed into verified motion plans.A Conditional Quantum Kac-Bernstein Theorem
We establish a conditional quantum Kac-Bernstein theorem for mixing of two independent, finite-mode bosonic inputs with quantum memories.A Bayesian-Geometric Reading of Classical Model-Based Optimal Design
The classical model-based optimal design criteria (D-, T-, L-, C-, and A-optimality) are usually motivated via the Cramér--Rao bound as optimizing scalar functions of the Fisher information matrix…Directional Statistics in Signal Processing: A Geometry Driven Overview of Models, Learning, and Applications
Many signal-processing objects are defined by periodicity, normalization, sign or global-phase ambiguity, basis invariance, or positive-semidefinite constraints and therefore cannot be modeled…Estimating "Realized" Skewness using Convolutional Neural Network
We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks…An Excursion into Grassmannian Spectra
We revisit the classical spectral problem for the Laplace-Beltrami and Laplace-Bochner operators on complex Grassmannians.Glauber Dynamics for Sampling Colorings: Rapid Mixing above $(1+1/\mathrm{e})Δ$ via Hidden Hard-Core Models
We show that for every $γ>0$ and every sufficiently large $Δ$, the Glauber dynamics for proper $q$-colorings of any $n$-vertex graph of maximum degree $Δ$ has spectral gap $Ω_γ(1/n)$, and hence…Finding Integer Roots of Polynomials in Nearly Optimal Time
Let $f\in\mathbb Z[x]$ be a polynomial of degree $n$ with integer coefficients of absolute value less than $2^b$.Asymptotic Optimality of $cμ$-Rule for LLM Inference Scheduling under Memory Constraint
Large language model (LLM) inference, the process by which a pre-trained model receives an input prompt and generates output tokens sequentially, has become increasingly important with the…Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks
Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology.Geometric Obstructions and Sharp Genus Dependence in LCD Scalings
We study the least pole order of a rational function whose nonzero evaluations scale a prescribed code to a Euclidean LCD code.Deep Neural Operator Learning for stochastic heat equations and applications
In this paper, we establish a universal approximation theorem for solution operators of semilinear stochastic heat equations in spatial dimension \(d\ge2\), extending deep neural operator…Substantially Improved Rate and Distance for Addressable Transversal Phase Gates
An important ingredient in fault-tolerant quantum computation is asymptotically good codes with fully addressable transversal non-Clifford gates.
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