Researchers at the National University of Defense Technology and Shanghai Jiao Tong University introduce a symmetry-aware neural network architecture that slashes multi-agent reinforcement learning training time by up to 70%, doubles predator–prey task performance, and boosts sample-efficient coordination for autonomous traffic control, cooperative robotics, and power-grid management.

Researchers from Hefei University of Technology and Tsinghua University have developed a new cognitive diagnosis approach that overcomes student-concept sparsity to boost accuracy by up to 6%, delivering more accurate, real-time personalized learning analytics and equitable feedback.

A new semi-supervised audio analysis model leveraging bi-path feature extraction and attention mechanisms boosts sound event detection accuracy by 12.7% (PSDS1 0.489, PSDS2 0.771), enabling smarter smart-home, security, wildlife and industrial monitoring.

A new study from the University of Colorado Anschutz Medical Campus shows that free, open-source artificial intelligence (AI) tools can help doctors report medical scans just as well as more expensive commercial systems without putting patient privacy at risk.

Computer scientists at the University of Rochester have developed an AI-powered, speech-based screening tool that can help people assess whether they are showing signs of Parkinson’s disease, the fastest growing neurological disability in the world. A study published in the journal npj Parkinson’s Disease introduces a web-based screening test that asks users to recite two pangrams—short sentences using all 26 letters of the alphabet. Within seconds, the AI analyzes the voice recordings for subtle patterns linked to Parkinson’s, with nearly 86 percent accuracy.

The machine-learning model outscores older methods and is being made available worldwide.