Bridging Neural Networks and Gaussian Processes for Scalable Uncertainty
![The study demonstrates the BlendedNet dataset’s capacity for accurate force coefficient ([latex]C_{f_{z}}[/latex]) prediction, as evidenced by the close alignment between test data and B-INN predictions, with normalized absolute error providing a quantitative measure of this performance.](https://arxiv.org/html/2601.22860v1/fig/blendednet_cfz.png)
A new Bayesian model combines the strengths of both approaches to deliver accurate and reliable predictions for complex physical systems.
![The study demonstrates the BlendedNet dataset’s capacity for accurate force coefficient ([latex]C_{f_{z}}[/latex]) prediction, as evidenced by the close alignment between test data and B-INN predictions, with normalized absolute error providing a quantitative measure of this performance.](https://arxiv.org/html/2601.22860v1/fig/blendednet_cfz.png)
A new Bayesian model combines the strengths of both approaches to deliver accurate and reliable predictions for complex physical systems.

A new dataset reveals the surprisingly short lifecycles of meme coins and offers tools to identify potential scams in the volatile world of cryptocurrency.

Researchers have developed a method to significantly improve the accuracy and reliability of machine learning models used to forecast complex fluid dynamics over extended periods.
![A framework for improving AI safety benchmarking-derived from an analysis of 210 existing benchmarks-identifies nine key concerns and ten recommendations centered on extending safety evaluations beyond familiar scenarios, employing probabilistic risk quantification-as expressed by [latex]P(risk)[/latex]-and ensuring measurements correlate with demonstrable real-world safety outcomes.](https://arxiv.org/html/2601.23112v1/x1.png)
Current AI safety benchmarks often fall short of accurately assessing real-world risks, prompting a need for more rigorous and nuanced evaluation methods.
A new review systematically deconstructs the mathematical foundations of today’s most powerful AI systems, offering a unified perspective on their inner workings.

A new benchmark assesses the ability of artificial intelligence to synthesize information from text, images, and videos for complex financial analysis.
Across fields as diverse as physics, biology, and social science, researchers are repeatedly stumbling upon the same mathematical tools to describe moments of dramatic transition.
New research reveals how both direct and indirect impacts from tropical cyclones can significantly reduce the accuracy of medium-range weather predictions.

Researchers have developed an advanced artificial intelligence system capable of forecasting solar flares with improved accuracy and interpretability.

A new analysis reveals that commonly used datasets for evaluating secret detection models are riddled with duplicated data, leading to inflated performance scores and a false sense of security.