The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations ...

Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement ...

Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models ...

AI-powered coding agents have significantly transformed software development in 2025, offering advanced features that enhance productivity and streamline workflows. Below is an overview of some of the ...

Large language models (LLMs) have become an integral part of various applications, but they remain vulnerable to exploitation. A key concern is the emergence of universal jailbreaks—prompting ...

Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling longer text is challenging ...

Answering open-domain questions in real-world scenarios is challenging, as relevant information is often scattered across diverse sources, including text, databases, and images. While LLMs can break ...

Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional ...

The fast development of wireless communication technologies has increased the application of automatic modulation recognition (AMR) in sectors such as cognitive radio and electronic countermeasures.

Artificial Neural Networks (ANNs) have their roots established in the inspiration developed from biological neural networks. Although highly efficient, ANNs fail to embody the neuronal structures in ...

Modeling biological and chemical sequences is extremely difficult mainly due to the need to handle long-range dependencies and efficient processing of large sequential data. Classical methods, ...

Protecting user data while enabling advanced analytics and machine learning is a critical challenge. Organizations must process and analyze data without compromising privacy, but existing solutions ...