Science

Researchers have developed a decoding framework that empowers large language models to self-assess and refine their outputs, dramatically reducing factual errors.

A new study shows that surprisingly compact language models can effectively identify multiple issues hidden within seemingly simple code changes.

A new approach dynamically adjusts model parameters during processing, boosting speed and reducing memory demands without sacrificing performance.

New analysis of heavy-ion collisions is refining our understanding of the fundamental state of matter at extreme temperatures and densities.

A new approach frames the validation of Intelligent Document Processing systems as a search-based software testing problem, prioritizing the discovery of diverse risk factors over achieving maximum accuracy.

Researchers have achieved a comprehensive solution for describing string behavior in a complex, three-dimensional space, bridging the gap between theoretical physics and concrete calculations.

A new theoretical analysis reveals fundamental constraints on compressing Chain-of-Thought reasoning within large language models, and proposes a method to overcome signal decay.

New research reveals how cleverly crafted prompts can bypass safeguards in large language models relying on in-context learning, posing a significant security risk.

A novel group key agreement protocol addresses the unique security challenges of resource-constrained cyber-physical systems relying on broadcast bus networks.

A new approach to system design leverages independent, validated models and real-time monitoring to guarantee state integrity and overcome the limitations of traditional machine learning.