Science

A new approach leverages the power of graph neural networks and data augmentation within a Transformer architecture to predict how to synthesize complex molecules.

New research shows that analyzing the connections between users and content can significantly boost the accuracy of fake news detection systems.

A new decentralized marketplace aims to unlock the power of shared data while safeguarding privacy and incentivizing participation.

Attackers are increasingly leveraging the hype around generative AI to disguise harmful browser extensions as legitimate tools.

A new analysis argues that large language models don’t reason so much as statistically predict the most plausible continuation of a given prompt.

A new study systematically investigates the potential of reinforcement learning to overcome key challenges in generating high-quality 3D models from text prompts.

New research demonstrates how sparse autoencoders can create easily understood representations of text, offering powerful tools for data analysis.

A new method, DCFO, clarifies why data points are flagged as outliers by the Local Outlier Factor algorithm, addressing key limitations in existing explanation techniques.

Researchers have developed a method that prompts large language models to reason backwards from potential answers, revealing gaps in incomplete questions and improving problem-solving abilities.

A new framework combines dynamic neural networks and adversarial learning to dramatically improve intrusion detection in next-generation wireless systems.