Science

Inspired by the principles of free-market economics, researchers have developed a novel optimization framework capable of discovering solutions in challenging and open-ended domains.

Researchers have developed a novel graph neural network that enhances the detection of money laundering by better analyzing transaction patterns.

Researchers have developed a novel framework that leverages dynamic relationships between stocks to improve the accuracy of trend forecasting.

New research demonstrates that even basic transformer architectures, when trained correctly, can effectively learn to mimic a wide range of more complex models.

A new approach leverages pre-trained propagation trees and Transformer networks to significantly improve the detection of social media rumors and overcome the limitations of traditional graph-based methods.

Researchers have developed a graph neural network framework that dramatically improves the speed and accuracy of simulating atomic interactions, paving the way for more realistic and efficient materials science.

New research explores how to build more stable and reliable AI trading agents by modeling the selective consensus strategies of human traders.

A new AI framework directly analyzes powder X-ray diffraction data to predict a material’s crystalline form with unprecedented accuracy.

A new study reveals how orchestrating multiple AI agents delivers superior accuracy and cost efficiency for complex financial document processing.

New research reveals how carefully crafted network traffic can evade machine learning-based intrusion detection systems protecting Internet of Things devices.