This paper investigates limitations of learning tanh neural networks under finite-precision computations and Lp accuracy guarantees.

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

This paper introduces and analyzes a consistent estimator for the sub-Gaussian parameter ($\xi_*^2$), providing convergence rates and demonstrating its applicability in large-scale biological enrichme…

This paper proposes Tail-Aware Adaptive-k (TAA-k), a training-free framework for adaptive context selection in retrieval-augmented generation systems using Extreme Value Theory.

This paper develops a novel, computationally efficient method to quantify the uncertainty in wide neural network predictions by characterizing the limiting random fluctuations using stochastic evoluti…

This paper proposes a reliability-aware framework to solve the fuzzy shortest path problem in directed graphs, optimizing routes based not only on cost but also on the reliability of the associated fu…

The paper proposes WEINCE, a modified InfoNCE objective that uses extreme value theory corrections to improve contrastive learning by more accurately modeling the selection of hard negative examples.

E4GEN introduces an explainable diffusion framework that significantly improves time-series generation by specifically focusing on and controlling the fidelity of extreme events.

This paper shows that standard optimal control in Markov Decision Processes (MDPs) with an absorbing catastrophic state naturally generates behavioral signatures mimicking prospect theory, even withou…

The paper analyzes the potential market impact of a large, unknown Bitcoin holder (the Satoshi overhang) and concludes that the mechanical downside risk is bounded, suggesting the terminal states are…

The paper establishes that the training process of fully connected deep neural networks (DNNs) on exponential family data is mathematically equivalent to performing a Renormalization Group (RG) calcul…

The paper analyzes robust covert wireless communication under bounded uncertainty, demonstrating that the adverse conditions governing reliability and covertness are distinct, thus requiring a conflic…

The paper introduces the linear canonical Riesz potential (LCRP) and analyzes its convergence properties, leveraging these findings to propose a novel, secure, and efficient asymmetric cascaded LCRP m…

The paper investigates forecasting sparse and bursty vulnerability sightings, concluding that traditional time-series models like SARIMAX are inadequate, and count-based methods like Poisson regressio…

The paper analyzes preference-shaped expected improvement criteria for Bayesian multiobjective optimization, precisely characterizing when transformations preserve key properties like exact computatio…

The paper develops a stochastic framework using Laplace-Carson transforms to model and quantify optimal proactive defense timing against a single cyberattack, providing closed-form solutions for defen…

The paper proposes a fuzzy modeling framework using subnormal Gaussian fuzzy numbers to prioritize IDS alerts by explicitly incorporating threat severity, detection confidence, and organizational risk…

The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…

The paper develops a minimal dynamical model showing that adaptive softmax routing in Mixture-of-Experts (MoE) layers can undergo abrupt transitions to load imbalance via bifurcation mechanisms.

The paper introduces a history-aware adaptive Reduced-Order Model (ROM) framework using incremental Singular Value Decomposition (iSVD) that maintains accuracy for online dynamics far beyond the initi…