Science

A new framework rigorously evaluates trading strategies based on market microstructure signals, focusing on out-of-sample performance and avoiding the pitfalls of overfitting.

New research reveals that artificial intelligence agents, when interacting in networked environments, exhibit a tendency to overestimate popular participation, mirroring human susceptibility to social influence.

As artificial intelligence increasingly influences economic forecasting, understanding why models make certain predictions is becoming as crucial as the predictions themselves.

As artificial intelligence systems gain increasing autonomy in financial markets, traditional risk management approaches are proving inadequate, demanding a new regulatory paradigm.

A new approach combining graph and transformer neural networks enhances the accuracy of identifying objects based on their acoustic signatures in challenging underwater environments.

A new approach leverages the power of Transformer networks to rapidly analyze gravitational microlensing data and detect faint signals of free-floating planets.

Machine learning is proving to be a powerful tool for astronomers, enabling the automated classification of high-energy sources in the universe.

A new training pipeline leverages reinforcement learning to enable conversational agents to improve their reasoning abilities by actively interacting with their environment.

New research reveals that making large language models more factually accurate doesn’t automatically make them more creative, and can even stifle their ability to generate novel ideas.

A new approach combines machine learning with principles of fluid dynamics to forecast power flow and ensure grid reliability.