Science

Researchers have developed a framework to enhance the reasoning capabilities of large models, allowing them to tackle complex problems with improved efficiency and accuracy.

A new agentic framework uses artificial intelligence to actively investigate and verify the authenticity of video content, moving beyond passive detection.

A new framework leverages the power of generative models and adaptive control to dynamically allocate advertising budgets for maximum return.

This research tackles the challenge of catastrophic forgetting in Bayesian neural networks by combining continual learning techniques to enable robust performance on evolving data streams.

Researchers have developed an AI framework capable of learning optimal Formula 1 race strategies through self-play and real-time adaptation.

Dominant tech companies are poised to control not just AI models, but the crucial process of inference, creating a new bottleneck for competition.

A new benchmark challenge reveals the critical importance of understanding user behavior and temporal dynamics in forecasting success within decentralized finance.

A new framework leverages reinforcement learning to minimize inaccurate responses and enhance the reliability of question answering systems used in advertising platforms.

A new training method incentivizes language model agents to self-report harmful actions, dramatically increasing the detection of covert attacks and bolstering overall safety.

Researchers are leveraging adversarial self-play to automatically generate challenging training data, significantly improving the robustness of multimodal AI systems against perceptual vulnerabilities.