Large reasoning models, often powered by large language models, are increasingly used to solve high-level problems in mathematics, scientific analysis, and code generation. The central idea is to simulate two types of cognition: rapid responses for simpler reasoning and deliberate,…
In this tutorial, we’ll explore how to create smart, multi-agent workflows using the Mistral Agents API’s Handoffs feature. This lets different agents work together by passing tasks to each other, enabling complex problems to be solved in a modular and…
Large Language Models (LLMs) generate step-by-step responses known as Chain-of-Thoughts (CoTs), where each token contributes to a coherent and logical narrative. To improve the quality of reasoning, various reinforcement learning techniques have been employed. These methods allow the model to…
Introduction: The Need for Dynamic AI Research Assistants Conversational AI has rapidly evolved beyond basic chatbot frameworks. However, most large language models (LLMs) still suffer from a critical limitation—they generate responses based only on static training data, lacking the ability…
In this tutorial, we introduce the Gemini Agent Network Protocol, a powerful and flexible framework designed to enable intelligent collaboration among specialized AI agents. Leveraging Google’s Gemini models, the protocol facilitates dynamic communication between agents, each equipped with distinct roles:…
What is the Model Context Protocol (MCP)? The Model Context Protocol (MCP), introduced by Anthropic in November 2024, establishes a standardized, secure interface for AI models to interact with external tools—code repositories, databases, files, web…
In this tutorial, we’ll demonstrate how to enable function calling in Mistral Agents using the standard JSON schema format. By defining your function’s input parameters with a clear schema, you can make your custom tools seamlessly callable by the agent—enabling…
A major hurdle in using AI for genomics is the lack of interpretable, step-by-step reasoning from complex DNA data. While DNA foundation models excel at learning rich sequence patterns for tasks such as variant prediction and gene regulation, they often…
Multi-agent systems are becoming a critical development in artificial intelligence due to their ability to coordinate multiple large language models (LLMs) to solve complex problems. Instead of relying on a single model’s perspective, these systems distribute roles among agents, each…
Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of…
