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…
