Quantum computing, despite its potential to outperform classical systems in certain tasks, faces a significant challenge: error correction. Quantum systems are highly ...

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are ...

Large Language Models (LLMs) have transformed artificial intelligence by enabling powerful text-generation capabilities. These models require strong security against critical risks such as prompt ...

Automated software engineering (ASE) has emerged as a transformative field, integrating artificial intelligence with software development processes to tackle debugging, feature enhancement, and ...

Artificial intelligence (AI) models have made substantial progress over the last few years, but they continue to face critical challenges, particularly in reasoning tasks. Large language models are ...

Drug discovery is a costly, lengthy process with high failure rates, as only one viable drug typically emerges from a million screened compounds. Advanced high-throughput (HTS) and ...

Deploying machine learning models on edge devices poses significant challenges due to limited computational resources. When the size and complexity of models increase, even achieving efficient ...

Generating high-quality, real-time video simulations poses significant challenges, especially when aiming for extended lengths without compromising quality. Traditionally, world models for video ...

Effective lesson structuring remains a critical challenge in educational settings, particularly when conversations and tutoring sessions need to address predefined topics or worksheet problems.

Large-sample hydrology is a critical field that addresses pressing global challenges, such as climate change, flood prediction, and water resource management. By leveraging vast datasets of ...

AI-driven solutions are advancing rapidly, yet managing multiple AI agents and ensuring coherent interactions between them remains challenging. Whether for chatbots, voice assistants, or other AI ...

Neural networks have traditionally operated as static models with fixed structures and parameters once trained, a limitation that hinders their adaptability to new or unforeseen scenarios. Deploying ...