A field on ReadSearch
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 35
Some minimal characteristics and flat trajectories of a system in the N-body problem
For the classical model of a system of N particles with interaction (the N-body problem) we consider some spatial problems of nonlinear programming for finding conditional minima and local extrema…SympFormer: Inertial Attention Blocks from Damped Hamiltonian Dynamics on Spaces of Probability Measures
The attention mechanism underpins modern large language models, yet their dynamics remain only partially understood.Reliable AI-Assisted Decisions via Selective Auditing
We consider online monitoring of an AI system that produces a sequence of decisions.Phase Transition from Random Features to Fully Trainable Networks
Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing.Operationalising Weaker Faithfulness: Recovery, Ambiguity and Computational Limits
Variables can predict an outcome jointly while each is individually independent of it, causing structure learners to discard informative links.Early learning in large residual networks
We study the early training dynamics of nonlinear residual networks with blocks of zero hidden layers as a model of more general residual networks.PnP*: A certifiable solver for Perspective-n-Point
Estimating a camera pose from 3D-2D correspondences - Perspective-n-Point - is one of the most fundamental problems in computer vision.Mimicking Volatilities under Marginal Constraints via $q_x$-Bass Martingales: A Parallel Computing Approach
We study the calibration of continuous-time martingale models to observed derivative prices via a dynamic martingale optimal transport problem that penalizes deviations from the volatility of a…Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome.Sharp Bounds on the Winner's Bias under Independent and Nonnegatively Correlated Noise
Reporting the best of $M$ noisy, unbiased scores overstates the winner's value by up to $c\sqrt{V}$, where $V$ is the total noise variance and the sharp constant $c$ depends on how the noise is…Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
Artificial intelligence (AI) is reshaping mechanical engineering (ME), from modeling, simulation, and optimization to diagnostics, design, and technical communication.On exact solutions to three-dimensional Yukawa lattice model and BCC to FCC phase transitions
We determine the global minimizers of the three-dimensional Yukawa lattice energy at every fixed density.A new lower bound for the sixth Schur number: $S(6)\ge 537$
We give an explicit partition of $\{1,\ldots,537\}$ into six sum-free sets, establishing $S(6)\ge 537$.A Jump-Modified Wasserstein Gradient Flow for Maximum Mean Discrepancy with Energy Kernels
We study a jump-modified Wasserstein gradient flow of the squared maximum mean discrepancy generated by the energy kernels $K(z)=-|z|^q$, $0<q<2$, in every dimension $d\ge2$ as well as the…Dynamics of Hidden Learning Structure
A learner's present predictions do not fully determine how it will learn next.StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves
Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help.ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM-Based Optimization
Large language models increasingly automate optimization modeling from natural language problems, producing written mathematics, solver code and a returned report.Curvature-Scaled Sharpness-Aware Minimization: Convergence Beyond Smoothness
Sharpness-Aware Minimization (SAM) has established itself as a widely used method for training deep neural networks.On the gradient conjecture at infinity: Finite spherical length for gradient trajectories of definable potentials
We prove that for every open $ U \subseteq \mathbb R^n $ the spherical projection of every gradient trajectory $θ(t)$, $t \geq 0$, of a definable $C^1$-potential $F \colon U \to \mathbb R$ has…Group Distributionally Robust Optimization over Decentralized Networks
We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk.
Get the few papers worth your time in Mathematics & Statistics every morning.
Choose your topics and ReadSearch reads the new papers in them each day, then brings you the few worth reading, each with its key finding in a sentence.