Science

A new data-driven approach enables safe control of complex systems, even when their internal dynamics are unknown.

Researchers have expanded the capabilities of automated protocol analysis to encompass the full algebraic power of Diffie-Hellman groups, unlocking more robust security proofs.

A new approach distributes the risk and responsibility of cryptographic key management, bolstering security for next-generation digital signatures.

New research establishes rigorous guarantees for finding optimal solutions in robust principal component analysis, even when dealing with challenging, non-convex problems.

A new architecture minimizes the performance overhead of homomorphic encryption, paving the way for faster and more practical privacy-preserving machine learning.

A standardized taxonomy for research software supply chains is crucial for consistently evaluating vulnerabilities and mitigating risks in academic and scientific computing.

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.