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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.
Topics in Mathematics & Statistics
Pure Math
- Commutative Algebra3 papers
- Algebraic Geometry27 papers
- Number Theory56 papers
- Rings & Algebras5 papers
- Representation Theory14 papers
- Quantum Algebra13 papers
- K-Theory & Homology
- Group Theory14 papers
- Operator Algebras24 papers
- Algebraic Topology17 papers
- Geometric Topology55 papers
- Metric Geometry14 papers
- Symplectic Geometry5 papers
- General Topology3 papers
- Logic17 papers
- Category Theory5 papers
Analysis & PDE
Applied Math
Probability & Statistics
Mathematical Physics
Newest papers
Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems
Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of an average of the…From What Is Used to What Is Missing: Rethinking Dynamic Rank in LoRA
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates.Energy-conserving scheme for a shallow-water model with capillary effects
We consider numerical approximations of shallow-water and thin-film flow models with capillary effects.Asymptotic-Preserving Tensor Neural Networks for 3D High-Field Kinetic Equations with Applications to Uncertainty Quantification
Kinetic equations in the high-field regime describe particle transport dominated by electric-field acceleration and collisional relaxation.Adversarial Multiplicity: Cherry-Picked Models and the Limits of Disclosure
Predictive multiplicity -- where similarly accurate models disagree on individual predictions -- can be leveraged to optimize secondary metrics (e.g., fairness) without loss in predictive performance.Rigorously Certified Neural-Network Computation of Nonlinear Water Waves at Near-Machine Precision
We present a computational framework that combines machine-learning-based discovery with rigorous computer-assisted numerical certification and formal verification for strongly nonlinear partial…Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting.Extensions of the Haldane gap proof: higher dimensions, near degeneracy, fermions and tensor-network states
A recent remarkable paper by OpenAI showed that the spin-one Heisenberg chain with periodic boundary conditions has a spectral gap in the thermodynamic limit.Spectral symmetry fingerprints in equivariant representations
Symmetry governs the organization of physical structures, while symmetry breaking reveals their transformations.Truncated backpropagation through time induces a two-sided gradient turnpike property
Truncated backpropagation through time reduces the computational burden of training recurrent models by restricting gradient computations to shorter temporal contexts.Information flow control in hybrid quantum networks
Quantum resources can change the dynamical features and operational behaviors of conventional networks.Statistical and Privacy Guarantees for Deep M-Regression via Stochastic Gradient Langevin Dynamics
In this article, we study deep nonparametric regression trained via stochastic gradient Langevin dynamics (SGLD), a variant of stochastic gradient descent that injects carefully calibrated Gaussian…Using Transformers to Generate 3-Manifold Triangulations
Using dehydration signatures as an encoding, we train GPT-2-style transformers to generate triangulations of 3-dimensional manifolds.Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
An experiment can be informative about the parameters of a working model and uninformative about whether that model is wrong.Identification and Bounding of Joint Expectation over Potential Outcomes
The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects.Learning and Steering in Incomplete-Information Games
In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing agents' utilities.A Bilevel Neurodynamic Approach for Distributed SCAD-Regularized Sparse Least Squares
This paper proposes a bilevel particle swarm optimization (PSO)-neurodynamic method for solving the distributed sparse least-squares problem regularized by the smoothly clipped absolute deviation…Projection-Free Gradient Tracking Neurodynamic for Distributed Nonnegative Sparse Optimization
This paper proposes a continuous-time, projection-free neurodynamic system based on gradient tracking for distributed nonnegative sparse optimization.A Universal Threshold Theory for Local Properties of Random Linear Codes
Thresholds for local properties tell us which guarantees to expect from random linear codes (RLCs) and help us carry those guarantees over to structured codes.Randomized Parallel Complexity of Weakly Convex Optimization
We study the minimum number of query rounds needed to find approximate stationary points of nonsmooth weakly convex functions, measured by the gradient of the Moreau envelope.
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