Beyond Credit Scores: Modeling Mortgage Risk with AI
A new approach combines the power of deep learning with interpretable statistics to better predict mortgage defaults and understand the factors driving credit risk.
Science
AI’s Cybersecurity Edge: Fragility and Adaptation
Artificial intelligence is transforming network defense, but its performance isn’t guaranteed in the face of evolving threats and real-world data challenges.
The Edge of Change: How Networks Reveal a System’s Capacity to Adapt
A new framework quantifies plasticity by linking network structure to dynamical regimes, offering a measurable way to understand a system’s responsiveness to change.
Untangling Cause and Effect: A New Framework for Complex Systems
![The system demonstrates that, given conditioning on variable 55, the distribution [latex]J^{\{5,6\}}\_{6}(\cdot\,|\,x\_{5};x\_{\{2,4\}})[/latex] becomes independent of variables 2 and 4, effectively nullifying the parent set of node 6-a consequence of the structural equilibrium model applied to the chain-connected anterial graph [latex]\mathcal{G}\_{1}[/latex]-and highlighting the propagation of error variable distributions within the interconnected system.](https://arxiv.org/html/2603.24859v1/x4.png)
Researchers have developed a graphical approach to reliably identify causal relationships in systems where multiple factors interact and confounding variables obscure the true drivers.
Building Robust AI: A New Approach to Multimodal Learning
![The study demonstrates variation across three distinct multimodal datasets[7], each offering unique samples reflective of inherent systemic differences in data representation.](https://arxiv.org/html/2603.25103v1/dataset_3_sample.png)
Researchers have developed a self-supervised learning framework that enhances the reliability of AI systems handling multiple data types, making them more resilient to errors and anomalies.
How Your Walk Reveals Your Health
New research shows a deep learning model can predict health risks across multiple body systems simply by analyzing 3D skeletal motion.
Smarter Traffic Lights: A New AI Approach for City-Wide Flow
Researchers have developed a novel reinforcement learning framework that dramatically improves traffic signal control, promising smoother commutes and reduced congestion in complex urban environments.
Decoding Emotion, Protecting Privacy
A new framework offers a path toward reliable depression detection from audio while prioritizing user data security.
Deeper Insights, Faster Training: Accelerating Bayesian Neural Networks
A new approach combines Nesterov acceleration with refined residual connections to dramatically improve the efficiency and accuracy of infinitely deep Bayesian neural networks.
The Hidden Cost of AI Safety Nets
As artificial intelligence increasingly assists in critical safety engineering tasks, a subtle degradation of human reasoning can occur – a phenomenon we call the ‘Competence Shadow’.