Science

A new framework combines the power of large language models with formal verification to create financial intelligence systems that are both accurate and demonstrably reliable.

A new study compares generative data augmentation techniques for improving predictions of slow transfer performance in scientific computing networks.

Researchers have developed a novel agentic framework that leverages the power of foundation models to predict how objects can be used without any prior training.

A new approach combines deep active learning with formal verification to generate targeted, diverse data for more efficient and robust model training.

A new analysis reveals the surprisingly limited impact of AI-generated deepfakes during the recent Canadian election, despite widespread concerns about their potential to disrupt democracy.

Researchers have developed a novel method to improve the reliability of brain-computer interfaces by addressing a hidden cause of performance decline in deep learning models.

A new review highlights the limitations of current methods for evaluating graph generative models, revealing a need for benchmarks that go beyond simple statistical comparisons.

A new review assesses the power of artificial intelligence to improve seasonal precipitation forecasting across the diverse landscapes of South America.

Researchers have developed a novel framework that actively seeks out anomalous patterns to identify deepfake videos, even those never seen before.

New research explores whether large language models can accurately simulate how consumers perceive and anticipate changes in pricing, offering insights into economic modeling and the potential biases of AI.