Vision-language models (VLMs) have become foundational components for multimodal AI systems, enabling autonomous agents to understand visual environments, reason over multimodal content, and interact with both digital and physical worlds. The significance of these capabilities has led to extensive research…

Every year, thousands of students take courses that teach them how to deploy artificial intelligence models that can help doctors diagnose disease and determine appropriate treatments. However, many of these courses omit a key element: training students to detect flaws…

Yandex has recently made a significant contribution to the recommender systems community by releasing Yambda, the world’s largest publicly available dataset for recommender system research and development. This dataset is designed to bridge the gap between academic research and industry-scale…

Policy gradient methods have significantly advanced the reasoning capabilities of LLMs, particularly through RL. A key tool in stabilizing these methods is Kullback-Leibler (KL) regularization, which discourages drastic changes between the current policy and the reference policy. While widely used…

Diffusion-based large language models (LLMs) are being explored as a promising alternative to traditional autoregressive models, offering the potential for simultaneous multi-token generation. By using bidirectional attention mechanisms, these models aim to accelerate decoding, theoretically providing faster inference than autoregressive…

In this tutorial, we demonstrate how to build an intelligent AI assistant by integrating LangChain, Gemini 2.0 Flash, and Jina Search tools. By combining the capabilities of a powerful large language model (LLM) with an external search API, we create…