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.

Researchers at Nanjing University and China Mobile unveil a novel privacy-preserving traffic-engineering algorithm that cuts multi-domain routing time by 24.35% while maintaining 90% link utilization through differential privacy and graph neural network–driven bandwidth prediction.

A new multilingual AI survey by Beijing Foreign Studies University researchers uncovers uneven training data, cross-lingual alignment challenges, and embedded bias in 50+ large language models—offering a roadmap for balanced corpora, universal representation, and robust bias mitigation to boost low-resource language support.

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 research team has developed an innovative remote sensing model that significantly improves the accuracy of monitoring nitrogen accumulation in wheat crops.

A research team now proposes a novel Multi-Granularity Alignment (MGA) domain adaptation framework that dramatically improves cross-domain detection accuracy, enabling deep learning models to maintain high performance across datasets collected in different countries and conditions.

A recent study published in Current Pharmaceutical Analysis provides a comprehensive bibliometric analysis of the application of multi-omics in drug discovery. The research highlights significant contributions from China and the United States and identifies emerging trends and hotspots, including personalized medicine, gut microbiota, artificial intelligence, and drug resistance. The study underscores the potential of multi-omics to transform disease treatment and improve patient outcomes.