The complex form of Vekua's characteristic factor: a derivation, and two sign corrections in §7 of Generalized Analytic Functions

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization

Non-normal spectral signatures of instability in neural network training dynamics

Optimization of randomized neural networks for transfer operator approximation

Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty

LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics

Learning Decision-Sufficient Representations for Linear Optimization

Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses

Prabhakar function and unified fractional kinetic equation in bicomplex space

Computing Gamma(p/q) with Beta function values

Flows on Graded Manifolds

Optimal embedding dimension in the Nash--Tognoli theorem

An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit

Sharp Bohr-Type inequalities for certain classes of close-to-convex functions

Invariants of real affine varieties based on their complexifications

Topological symmetric and braid homologies

A Formal Graph-Theoretic Framework for Pitch Class Set Analysis

Finite groups with high commuting probability for Sylow subgroups

Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems

Real 2-blocks in quasi-simple groups

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations

LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws

On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy

Training-Free Looped Transformers

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer

Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries

Asymmetric Scaling Laws from Sparse Features

Is Dimensionality a Barrier for Retrieval Models?

RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs

Commutator-Induced Uncertainty in VAEs

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them

Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating

Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection

Instance-Optimal Estimation with Multiple LLM Judges on a Budget

Entropy Equivalence Testing

Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation

Any-Dimensional Invariant Universality

Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models

Anytime Training with Schedule-Free Spectral Optimization

Concise and elegant proofs of three formulas for complete Bell polynomials

On Reed-Muller subcodes, Grassmannian partitions and sum-free functions

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation

Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology

The General Theory of Localization Methods

A Comprehensive Study of Clique Graphs and Clique Regular Graphs

Every signed planar graph is $5$-choosable: A short proof and refinements

General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions

PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels

Proximal basin hopping: global optimization with guarantees

Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches

On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients

Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space

Conditional Entropy of Heat Diffusion on Temporal Networks

The Geometry of Cooperative Game Solutions: Stratified Egalitarian Shapley Values

An Axiomatic Theory of Tie-Breaking: Impossibility, Characterization, and Decomposition

Amortized Energy-Based Bayesian Inference

PyCSP3-Scheduling: A Scheduling Extension for PyCSP3

Strategic PAC Learnability via Geometric Definability

Proximal-Based Generative Modeling for Bayesian Inverse Problems

Every Minimal Counterexample to the Erdős-Gyárfás Conjecture is Predominantly Cubic

SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference

NOVA: Fundamental Limits of Knowledge Discovery Through AI

Model-based Bootstrap of Controlled Markov Chains

TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects

Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems

Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes

Towards an Inferentialist Account of Information Through Proof-theoretic Semantics

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

Robust and Fast Training via Per-Sample Clipping

Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data

QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems

Information-Theoretic Measures in AI: A Practical Decision Guide

Inference of Online Newton Methods with Nesterov's Accelerated Sketching

A Unified Fractional Regularization Framework for Sparse Recovery

Mathematical Foundations for Peer-to-Peer Lattice Computation

Geometric Layer-wise Approximation Rates for Deep Networks

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory

ML-based approach to classification and generation of structured light propagation in turbulent media

Zeroth-Order Optimization at the Edge of Stability

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables

Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes

Training-Free Rate-Distortion-Perception Traversal With Diffusion

Conformal Policy Control

Linear Regression with Unknown Truncation Beyond Gaussian Features

ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport

Feature Learning Dynamics in Infinite-Depth Neural Networks

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms

Normalizing Flows on Quotient Manifolds via Boundary Quotients

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks

What Can Be Recovered Under Sparse Adversarial Corruption? Assumption-Free Theory for Linear Measurements

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis

Program Evaluation with Remotely Sensed Outcomes

Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries

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