Science

Researchers have developed a novel framework to more effectively identify complex money laundering networks by focusing on relevant data and streamlining analysis.

New research demonstrates a practical mechanism for approximating optimal gains-from-trade in complex matching markets, paving the way for more effective market design.

New research using large-scale simulations reveals an evolutionary basis for successful trend-following strategies in financial markets, challenging common assumptions about investor behavior.

New research demonstrates a method for identifying and removing memorized data from large language models used in financial forecasting, leading to more robust and reliable predictions.

Researchers have developed a novel framework leveraging tensor networks to dramatically accelerate the pricing of complex financial derivatives, particularly American options.

A new framework combines the power of deep learning with established transportation theory to forecast traffic volumes on national road networks.

New research reveals that traditional methods for detecting reentrancy vulnerabilities in smart contracts are faltering, as Large Language Models emerge as a surprisingly effective defense.

Researchers have developed a self-supervised framework that spots manipulated audio-visual content by analyzing inconsistencies between facial movements, speech, and inherent visual artifacts.

A new approach uses genetic algorithms to directly design convolutional neural networks that are more resilient to carefully crafted inputs designed to fool them.

New research reveals that user satisfaction with leading AI assistants isn’t driven by raw technical power, but by factors like usability and platform integration.