Researchers have developed a novel generative AI model, called Collaborative Competitive Agents (CCA), that significantly improves the ability to handle complex image editing tasks. This new approach utilizes multiple Large Language Model (LLM)-based agents that work both collaboratively and competitively, resulting in a more robust and accurate editing process compared to existing methods. This breakthrough allows for a more transparent and iterative approach to image manipulation, enabling a level of precision previously unattainable. The findings were published on 15 November 2025 in Frontiers of Computer Science, co-published by Higher Education Press and Springer Nature.

Cognitive Diagnosis (CD) plays a crucial role in personalized learning by evaluating students' mastery of various concepts. However, current CD models face a significant challenge—the "student-concept sparsity barrier." This occurs when students have limited interactions with certain concepts.

The privacy protection issue of multi-domain traffic engineering has always been a hot topic. Consider that domains from different service providers may not be willing to expose the topology information within their own domains for security and privacy reasons.

Multilingual Large Language Models (MLLMs) have achieved remarkable success in advancing multilingual natural language processing, enabling effective knowledge transfer from high-resource to low-resource languages. Despite their achievements, MLLMs still face numerous issues and challenges, which can be categorized into three main aspects: corpora, alignment, and bias.

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design.

Network security has always been the focus of Internet research. In order to better detect these attacks, it is necessary to more accurately evaluate the state performance of the network. The existing measurement methods often only focus on a single or very few indicators.

Researchers from Tianjin University have introduced the Emergency Medical Procedures 3D Dataset (EMP3D), a pioneering resource that captures the intricate movements of medical professionals during life-saving interventions with unprecedented precision. Published on 15 November 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature, this dataset leverages synchronized multi-camera systems, advanced AI algorithms, and rigorous human validation to create the first 3D digital blueprint of emergency medical workflows. The innovation holds the potential to fundamentally transform emergency medical training and enhance robotic support in healthcare settings.