Science

Researchers have developed a system using artificial intelligence to proactively investigate and map the infrastructure behind increasingly sophisticated job scam operations.

New research reveals that negative spillover effects ripple through the cryptocurrency ecosystem, impacting assets across different blockchains as investors shift capital in response to market downturns.

As artificial intelligence increasingly integrates with critical infrastructure, a holistic, lifecycle-based security approach is essential to mitigate emerging threats.

A new framework leverages large language models to unify control and reasoning across radio access and core networks, promising more adaptable and efficient future networks.

Researchers have developed a method to distill complex neural networks into simpler, more understandable models by actively testing and removing redundant components.

A new framework combines the strengths of statistical learning and symbolic reasoning to move medical AI beyond simple prediction and towards robust, interpretable decision support.

New research explores the dynamics of artificial intelligence agents interacting on networks, revealing how their behaviors and the nature of shared information shape collective outcomes.

Effective fraud detection in blockchain relies heavily on the performance of Graph Neural Networks, but achieving optimal results requires careful attention to initialization and normalization techniques.

New research reveals how to trace the origins of large language model responses – whether they stem from learned knowledge or provided context.

Researchers are applying the principles of natural language processing to electrocardiogram (ECG) data, creating models that ‘understand’ heart rhythms and improve disease detection.