Science

A new framework leverages the spectral properties of system transitions to create more efficient and robust reinforcement learning agents.

A new approach combines elicitability theory with neural networks to efficiently solve complex stochastic equations arising in multi-agent systems.

New research demonstrates a significant leap in long-term stock market prediction accuracy using an optimized machine learning model.

Researchers have developed a deterministic system that identifies and explains sustained price fluctuations in equity markets, linking them to real-world events.

A new system, NoveltyRank, aims to move beyond simple citation counts and provide a more nuanced assessment of how truly novel a given AI paper is.

Researchers have developed a novel framework, PyFi, to enhance how AI models interpret complex financial images and generate insightful reasoning.

A new framework combines the power of large language models with formal verification to create financial intelligence systems that are both accurate and demonstrably reliable.

A new study compares generative data augmentation techniques for improving predictions of slow transfer performance in scientific computing networks.

Researchers have developed a novel agentic framework that leverages the power of foundation models to predict how objects can be used without any prior training.

A new approach combines deep active learning with formal verification to generate targeted, diverse data for more efficient and robust model training.