This review introduces a framework that combines deep learning with committor analysis and explainable AI (XAI) to systematically identify reaction coordinates in complex molecular systems. The approach enables the quantitative assessment of individual input variable contributions, enhancing the interpretability of molecular transition pathways.

This paper explores lightweight Generative AI (GenAI) models for network traffic synthesis to address data scarcity and privacy in Network Traffic Classification (NTC). It evaluates transformer-based, state-space, and diffusion models, demonstrating their effectiveness in generating high-fidelity synthetic traffic for training and augmenting NTC systems.

This paper introduces POP-CORN, a novel neural network-based tool for automatically detecting coronal hole boundaries in solar extreme ultraviolet images. By incorporating categorical features of large-scale solar structures, the model accurately determines optimal intensity thresholds for consistent coronal hole identification across different solar cycles, offering a significant advancement in space weather forecasting.

Trustworthy AI systems increasingly rely on probabilistic computation, shifting performance bottlenecks from arithmetic to memory, which must deliver both data and randomness. This paper introduces a unified data-access perspective, treating deterministic access as a limiting case of stochastic sampling, to analyze and address these new memory challenges.

A novel dynamical systems model predicts a critical AI capability threshold, beyond which human skills abruptly collapse due to delegation, a phenomenon termed the "enrichment paradox." The model suggests that periodic AI failures and mandatory practice can paradoxically preserve human capabilities and highlights the irreversible nature of profound skill loss.