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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 30
Conformal Prediction with Stable Measurement Mechanisms
In applications such as air-quality monitoring, inexpensive sensor readings can help predict pollutant concentrations measured by a separate reference instrument.On the uniqueness of translation-invariant ground states of the $S=1$ Heisenberg and $J$--$D$ antiferromagnetic chains
Using the analytic machinery and numerical inputs of OpenAI's proofs of the Haldane gap, we prove uniqueness of the translation-invariant ground state of the infinite $S=1$ Heisenberg…A Reduction Bound for Entanglement Detection by Positive Free Maps
Collins, Hayden, and Nechita used free probability to construct families of $k$-positive but not $(k+1)$-positive maps and applied them to entanglement detection.Optimal ratcheting dividend policies with resets
We study an optimal dividend policy problem under ratcheting and bankruptcy constraints.Inference for Causal Path-Specific Effects in Longitudinal Studies with Repeated Treatments and Multiple Mediators
In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates.Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization
We consider multiblock nonconvex and nonsmooth optimization without assuming bounded iterates or coercivity of the original objective.Online Discrepancy of Sparse Random Vectors over Long Horizons
We study online vector balancing for independent sparse random vectors: each arrival is supported on a uniformly random set of $s$ of the $n$ coordinates, with nonzero entries equal to one (binary…Non-Ergodic Markovian Mirror Descent with Unbounded Noise
We analyze stochastic mirror descent for minimizing a convex objective expressed as an expectation under the stationary distribution of a Markov chain, using noisy subgradient observations along a…Extrapolating learned dynamics across bifurcations based on generalized synchronization
Predicting dynamics across bifurcations by extrapolating trained models is crucial in diverse fields of physics.Rigorous Lower Bounds for Atomic and Ionic Ground-State Energies
We establish a new iterative method for computing rigorous lower bounds for the ground-state energies of all atoms and ions.Path integral control in the complex plane
Path integral control is a stochastic optimal control framework that computes optimal policies as weighted averages over state trajectories.Intrinsic Gradient Barriers in Pushforward Deep Generative Models
Broad classes of deep generative models (DGMs) operate by learning to map a simple source distribution, such as a standard Gaussian, to a complex high-dimensional target distribution.Certifying Distributionally Robust Decisions from Projection Observations
A scalar measurement can determine a robust decision without identifying the latent covariance or every decision risk.The German Tank Problem & Navigating by Scent
A robot searching for the source of an odor must eventually answer a notably hard question: have I arrived?Sparse Sum-of-Squares Layers for Certified Learning
A central limitation of modern machine learning systems is the lack of formal certificates guaranteeing their behavior beyond empirical, sample-based evaluation.Stochastic quantization and Osterwalder-Schrader axioms for quantum field theory models
This PhD thesis constructs the $P(Φ)_2$ Euclidean quantum field theory on $\mathscr{S}'(\mathbb{R}^2)$.Semiclassical dynamics of higher-order squeezing: self-adjoint extensions and coherent returns
Higher-order squeezing Hamiltonians have several self-adjoint realizations, while their classical trajectories reach infinity in finite time.Faithful Federated Tabular Data Synthesis
Synthetic data is an important data source in the AI era.Online Covariance Estimation for Averaged Stochastic Gradient Descent on Riemannian Manifolds
We study online inference for averaged Riemannian stochastic gradient descent (RSGD) when the unknown parameter lies on a manifold.Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks
Compiling implied volatility (IV) surfaces and forecasting derivative risk from high-frequency equity and options feeds requires addressing contract heterogeneity, schema shifts, volatility…
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