Science

A new generation of AI-powered research agents is dramatically improving the identification of promising drug assets worldwide.

A new agentic framework empowers anomaly detection systems to reason through time series data and diagnose issues with improved accuracy and interpretability.

A new study reveals the growing – and detectable – presence of AI-assisted content within Turkish news reporting.

Researchers have developed a novel agent-based system that automatically discovers and refines investment factors, promising a new approach to quantitative trading.

A new framework, WebClipper, dramatically improves the efficiency of web-based AI agents by intelligently eliminating unproductive exploration paths.

A new method allows generative models to not only create content but also reliably identify when they’re venturing into uncharted territory.

Researchers have developed a method to dramatically speed up graph analysis by moving computations from discrete nodes to a continuous space, unlocking new possibilities for large-scale graph machine learning.

Researchers have released a comprehensive suite of environments to rigorously evaluate algorithms designed for complex, large-scale multi-agent systems.

As large language models become increasingly sophisticated, distinguishing between human and machine authorship is becoming a critical challenge.

New research reveals how competitive experimentation in dynamic pricing can inadvertently push prices higher, even without explicit collusion.