Science

Researchers have developed a learning-based control system that dramatically improves the speed and reliability of impact wrench operation.

A new benchmark reveals that large language models are often overconfident in their predictions and struggle to accurately assess their own uncertainty.

A new approach uses sparse dictionary learning to classify the elusive equation of state governing the behavior of neutron stars by analyzing gravitational waves from simulated mergers.

A new systematic analysis reveals that even the most advanced language models exhibit and perpetuate significant biases across political, cultural, and social domains.

A new framework aims to address the critical lack of reproducibility in predictive process mining, offering a path toward more reliable and comparable model evaluations.

A new approach to machine learning allows researchers to improve diagnostic accuracy for collagen VI-related dystrophies without compromising patient privacy.

New research connects observations of dual active galactic nuclei with the expected gravitational wave signals from merging supermassive black holes, offering a path towards multi-messenger astronomy.

Researchers have developed a powerful pipeline leveraging artificial intelligence to accurately model complex X-ray spectra and determine underlying physical parameters.

New research demonstrates how combining artificial intelligence with vulnerability data can significantly improve the prediction of zero-day exploit severity.

A new approach allows engineers to translate imprecise, natural language descriptions of system behavior into a formal, verifiable logic.