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 techniques that bypass safeguards, allowing users to access restricted information. These exploits can be used to facilitate harmful activities, such as synthesizing illegal substances or evading cybersecurity measures.… →

Large-scale language models (LLMs) have advanced the field of artificial intelligence as they are used in many applications. Although they can almost perfectly simulate human language, they tend to lose in terms of response diversity. This limitation is particularly problematic in tasks requiring creativity, such as synthetic data generation and storytelling, where diverse outputs are… →

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 down complex queries into simpler steps to improve retrieval, they usually fail to account for how data is structured, leading to suboptimal results. Agentic RAG introduces iterative retrieval, refining… →

OpenAI has introduced Deep Research, a tool designed to assist users in conducting thorough, multi-step investigations on a variety of topics. Unlike traditional search engines, which return a list of links, Deep Research synthesizes information from multiple sources into detailed, well-cited reports. This feature is particularly useful for professionals in fields such as finance, science,… →

Traditional approaches to training language models heavily rely on supervised fine-tuning, where models learn by imitating correct responses. While effective for basic tasks, this method limits a model’s ability to develop deep reasoning skills. As artificial intelligence applications continue to evolve, there is a growing demand for models that can generate responses and critically evaluate… →

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. With their various modulation types and signal changes, modern communication systems provide significant obstacles to preserving AMR performance in dynamic contexts.  Deep learning-based AMR algorithms have emerged as the leading… →

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, particularly Transformer-based architectures, are limited by quadratic scaling in sequence length and are computationally expensive for processing long genomic sequences and protein modeling. Moreover, most existing models have in-context… →