Real-world networks, such as those in biomedical and multi-omics datasets, often present complex structures characterized by multiple types of nodes and edges, making them heterogeneous or multiplex. Most graph-based learning techniques fail to handle such intricate networks because of their intrinsic complexity, even though graph neural networks have been quite in vogue and garnered significant… →

Function calling has emerged as a transformative capability in AI systems, enabling language models to interact with external tools through structured JSON object generation. However, current methodologies face critical challenges in comprehensively simulating real-world interaction scenarios. Existing approaches predominantly focus on generating tool-specific call messages, overlooking the nuanced requirements of human-AI conversational interactions. The complexity… →

Diffusion models have pulled ahead of others in text-to-image generation. With continuous research in this field over the past year, we can now generate high-resolution, realistic images that are indistinguishable from authentic images.  However, with the increasing quality of the hyperrealistic images model, parameters are also escalating, and this trend results in high training and… →

Red teaming plays a pivotal role in evaluating the risks associated with AI models and systems. It uncovers novel threats, identifies gaps in current safety measures, and strengthens quantitative safety metrics. By fostering the development of new safety standards, it bolsters public trust and enhances the legitimacy of AI risk assessments. This paper details OpenAI’s… →

Large Language Models (LLMs) have revolutionized the field of artificial intelligence, demonstrating remarkable capabilities in various tasks. However, to fully harness their potential, LLMs must be equipped with the ability to interact with the real world through tools. As the number of available tools continues to grow, effectively identifying and utilizing the most relevant tool… →