Researchers have developed a deep learning model that leverages the power of graph neural networks and transformers to more accurately forecast how stocks move in relation to each other.
A new framework systematically evaluates how changes in underlying data relationships impact the reliability of fairness interventions in machine learning systems.
Researchers are using the power of artificial intelligence to simulate how individuals adopt preventative behaviors during an epidemic.
New research demonstrates how artificial intelligence can automate the critical process of identifying security policy weaknesses after a cyberattack.
![The FLNet model establishes a novel architecture for [latex]f(x) = w^T x[/latex], enabling efficient and scalable feature learning through a learned network of weights, <i>w</i>, and input features, <i>x</i>.](https://arxiv.org/html/2601.03884v1/x1.png)
A new deep learning pipeline leverages freely available satellite data to pinpoint flood damage at the individual farm level, offering a cost-effective solution for disaster response and agricultural monitoring.
Research reveals how data from our smartphones, combined with advanced artificial intelligence, can predict changes in mental health with increasing accuracy.
![The study demonstrates that compounding data and weight recursion-where each generation of synthetic text refines training from the previous generation’s weights-results in measurable drift, quantified as the change in [latex]\Delta U_{\mathrm{LLN,cov}}(\delta)[/latex] and [latex]\Delta G_{\mathrm{KF}}(\delta)[/latex], relative to a baseline checkpoint.](https://arxiv.org/html/2601.03385v1/x2.png)
New research offers a powerful framework for understanding and predicting when large language models begin to lose coherence during prolonged, synthetic data training.
New research reveals that financial networks exhibit surprisingly fragile stability, with even modest increases in interconnectedness potentially triggering cascading defaults.
New research models the dynamic interplay between artificial intelligence, physical capital, and the workforce in China, revealing both opportunities and risks for sustainable growth.
A new comparative analysis reveals fundamentally different approaches to AI governance across the globe, driven by divergent institutional priorities and understandings of ‘AI safety’.