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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 3
The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?
The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing?Low-Temperature Sampling via Equilibrium Disagreements
We develop a new framework for sampling at low temperatures using Glauber dynamics.Data-driven discovery of intrinsic Lie point symmetries of differential equation systems
Discovering continuous symmetries directly from differential-equation data can reveal geometric structure for model reduction and scientific machine learning without symbolic determining equations…Polynomial-time classical simulation of boson and fermion sampling under thermal loss
Sampling experiments are a leading approach to demonstrating quantum computational advantage, yet their robustness to experimental noise is not completely understood.Tusnády's problem and online two-sample thinning
We determine the sharp order of discrepancy in Tusnády's problem in every fixed dimension $d\ge 2$.Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness
Adversarial Training (AT) is an important method for improving model robustness.Provable circuit-level thresholds for non-abelian topological phases
We provide an explicit scheme to realize a quantum memory in any 2D non-chiral topological order.Discovering like a scientist: Literature-informed dimensionless learning for physical closure of data-driven equations
Data-driven methods can recover compact equations, yet the physical dependence of their coefficients across operating conditions often remains unresolved.Near linear mixing of random-cluster Glauber dynamics on random regular graphs
We study the random-cluster model on locally treelike graphs at low temperatures.Near-Optimal Complexity of Finding Approximate Local Minima of Nonconvex Finite Sums
We study the component-oracle complexity of finding approximate local minima of nonconvex finite sums under mean-squared averaged smoothness with parameter $L$ and a $ρ$-Lipschitz average Hessian.Towards the Query Complexity of Sampling under Strong Log-Concavity and Log-Sobolev Inequalities
We study the query complexity of sampling from smooth distributions on $\mathbb R^d$ using first-order oracles.Functional inequalities from the fundamental limits of Bayesian estimation
We develop a Bayesian information-theoretic framework for proving functional inequalities for log-concave measures.Pathwise-Budgeted LLM Routing: Online Allocation with K-Bit Tail-Cost Learning
Firms using large language models under fixed budgets must decide which model to call and whether to retry a failed task.A Complete Characterization of $q$-Ary $2$-Designs in Doubly-Extended Reed-Solomon Codes
Let $\mathcal{C}_k$ be the doubly-extended Reed--Solomon code with parameters $[q+1, k, q-k+2]_q$.On Convergence of Gradient Temporal-Difference Learning with Regularized Corrections and Eligibility Traces
Temporal-difference (TD) learning underlies many prediction and control algorithms, but can diverge when combined with off-policy learning, bootstrapping, and function approximation.Does Your Trial Simulator Know What the Drug Does? An Intervention-Blind Test of Interventional Validity on 7,207 Registered Trials
Patient world models are increasingly proposed as simulators of clinical trials, and recent surveys rank their capabilities on a ladder from temporal prediction to counterfactual rollouts.Magnetic Bloch eigenvalue problems in nonuniform periodic magnetic fields: a Fourier-finite difference approach
We study magnetic Bloch eigenvalue problems for two-dimensional magnetic Schrödinger operators with nonuniform periodic magnetic fields and periodic scalar potentials, and develop a hybrid…Prior-data fitted networks for variable selection
Prior-data fitted networks (PFNs) have become state-of-the-art for tabular prediction but remain underexplored in other areas of statistics.How Unbalanced Must a Latin Rectangle Be?
We view a $k\times n$ Latin rectangle as $n$ points in the grid $[n]^k$.Attraction-Ranking Mismatch in Out-of-Distribution Detection: Theory and Evidence from Hamiltonian Feature Dynamics
Attracting features toward learned in-distribution (ID) regions is an intuitive strategy for out-of-distribution (OOD) detection, but does such attraction improve detection?
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