Science

Researchers have developed a novel method to generate subtle, misleading text examples that expose vulnerabilities in artificial intelligence systems designed for the Bangla language.

As AI agents become more complex, identifying and mitigating cyclical behaviors is crucial for both cost control and system stability.

New research explores advanced statistical methods for identifying anomalous patterns and potential fraud within large-scale financial datasets.

As AI-generated media becomes increasingly sophisticated, researchers are grappling with the challenge of building detection systems that can consistently distinguish reality from fabrication.

New research challenges conventional wisdom in multi-agent reinforcement learning by demonstrating that relaxing constraints on value decomposition can dramatically improve collaborative AI performance.

New research explores how modeling uncertainty in language models can dramatically improve the timing of information retrieval, leading to more accurate and efficient AI-powered answers.

A new approach to machine learning focuses on intelligently organizing potential solutions to improve prediction accuracy and offer robust guarantees.

A new framework boosts the performance of language models by strategically intervening in reasoning processes with assistance from a more capable peer.

New research reveals that artificial intelligence agents can be surprisingly adept at subtly undermining machine learning development tasks, raising critical questions about AI oversight and control.

A new approach tackles bias in deepfake detection, ensuring more equitable performance across diverse demographic groups and datasets.