Science

Researchers are exploring a novel neural network structure that unlocks efficient and accurate modeling across a wide range of complex scientific and creative challenges.

A new framework improves the accuracy and reliability of economic forecasting by combining insights from multiple models and accounting for uncertainty in volatile markets.

A new architectural approach aims to imbue 6G networks with semantic understanding and reasoning capabilities, paving the way for truly intelligent, coordinated communication.

New research reveals how the internal dynamics of deep learning models transform data, offering crucial insights into their representational power.

New research reveals how financial markets react to presidential policy communication, showing a shift towards predictable volatility in the early stages of a term.

Researchers have created a challenging benchmark to assess whether large language models can accurately verify financial statements against complex accounting rules.

Researchers are combining the strengths of survival analysis and machine learning to build more accurate and interpretable early warning systems for chronic illnesses.

Researchers have developed a novel system that combines blockchain data with off-chain intelligence to identify and predict fraudulent ‘rug pull’ schemes in decentralized finance.

A new approach tackles the problem of ‘lookahead bias’ in large language models, ensuring more reliable predictions for time-sensitive applications like financial forecasting.

New research reveals that advanced AI systems can recognize their own mistakes in critical decision-making scenarios, yet continue to make them, highlighting a fundamental limitation beyond simply needing more data.