Science

New research reveals a significant performance drop-off when applying deep learning and large language models to detect vulnerabilities in real-world code.

New research shows that combining cognitive principles with agent-based systems yields more reliable long-form content than simply increasing model size.

A new review reveals that data quality, implementation, and financial expertise are more critical to successful reinforcement learning in finance than sophisticated algorithmic design.

Even when grounded in structured knowledge, large language models can still generate factually incorrect information – this research explores why and offers a new approach to detecting these ‘hallucinations’.

A new approach uses reinforcement learning to significantly reduce factual errors and improve the consistency of answers from large language models, across both quick queries and in-depth explanations.

A new approach combines reinforcement learning and equilibrium concepts to optimize investment portfolios even when faced with sudden, unpredictable market shifts.

A new analysis assesses how well artificial intelligence tools can automatically pull crucial data from the ever-growing body of materials science research.

Despite impressive gains in artificial intelligence, new research reveals that even the most advanced language models struggle with fundamental biases and strategic inconsistencies during complex negotiations.

Researchers have developed a novel framework, BugSweeper, that leverages graph neural networks to pinpoint vulnerabilities within smart contract code with greater precision.

Researchers are harnessing the power of machine learning to isolate the faint signals from the universe’s first stars and galaxies.