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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 46
Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means
We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has applications to quantum state tomography, the…Dimension-Free PAC-Bayes Generalization for Deep Neural Networks via Dirichlet Processes
Classical PAC-Bayesian generalization theory places prior distributions over Euclidean parameter space $Θ\subseteq \mathbb{R}^D$, causing relative entropy to scale with network dimension $D$.Online Quantile Omniprediction for Proper Losses
The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omniprediction.Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options
To enable well-informed customer choices and redistribute demand across service options, providers can display probabilities regarding predicted service levels at the time of booking, effectively…Controlling the False Discovery Rate via Model-Agnostic Importance Measures
Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models.Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory
Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them.Operator-Space Entanglement Entropy in Random Fermionic Gaussian Dynamics
We study the von Neumann operator-space entanglement entropy (OSEE) of initial product operators evolved under random gaussian dynamics, as modeled by a Brownian SYK$_2$ model.Quantum-compatible Riemannian conjugate gradient for ground-state preparation
Riemannian optimization provides a natural geometric framework for designing quantum algorithms for ground-state preparation, most notably through quantum imaginary-time evolution.Feeling the generalised rough SABR boundary
We provide an in-depth analysis of a generalised rough version of the SABR volatility model, widely used in the financial industry.Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes.The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs.A complete classification of perfect state transfer in Grover walks on abelian Cayley graphs
We completely classify connected Cayley graphs over finite abelian groups that exhibit perfect state transfer in Grover walks.Why Shallow Neural Networks Learn Sinusoidal Components First: An Orthogonal Polynomial Dynamics Framework
Neural networks often learn low-frequency components before high-frequency ones --- a phenomenon called spectral bias or termed F-principle.Deep Learning of Topological Equivalence Classes
We demonstrate an application of deep learning to the problem of identifying whether two generating vectors correspond to topologically equivalent finite group actions on Riemann surfaces, showing…Extending the Oracle Approximating Shrinkage to General Distributions
The Oracle Approximating Shrinkage (OAS) estimator provides a closed-form, data-driven shrinkage intensity for regularizing sample covariance matrices toward a scaled identity target, but its…Per-State Signatures and Mean Time-to-Failure: An Algorithm for Systems With Simultaneous Failures
We present an algorithm that computes the structural signature and the mean time-to-failure (MTTF) of a monotone binary system at every one of its states, rather than only at the state in which all…Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring
Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs.Connectivity Bounds for GHZ Graphs
Greenberger-Horne-Zeilinger (GHZ) states are multipartite entangled quantum states that play a central role in quantum information theory, with applications in quantum communication, cryptography…Classification of bipartite observables III: simplicity, mixtures and quantum error correction
This is the third of a series of three papers that bring model theory to quantum information.Query-Efficient Rejection Sampling for Parallel Masked Diffusion
Masked diffusion models (MDMs) accelerate generation by unmasking multiple tokens in parallel, but sampling these tokens independently ignores their correlations and biases the output.
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