Vladimir Dyachkov PhD

Understanding AI-Driven Antitrust and Competition Law The rise of artificial intelligence (AI) in market economics has created a new frontier for antitrust and competition law. As businesses increasingly adopt AI-driven pricing algorithms, the potential for algorithmic collusion emerges, raising complex legal questions. This article explores how AI impacts competition law in the U.S. and EU, ➡️➡️➡️

In today’s fast-paced business environment, organizations are constantly looking for ways to optimize their use of technology, especially when it comes to artificial intelligence (AI) and large language models (LLMs). One innovative solution that has emerged is RouteLLM, a framework designed to help businesses maximize the efficiency of their language model applications while keeping costs ➡️➡️➡️

The Challenge of Fine-Tuning Large Language Models Fine-tuning large language models (LLMs) has always been a resource-intensive task that requires vast amounts of labeled training data. Traditionally, creating high-quality datasets often involves collecting hundreds of thousands of examples, most of which are irrelevant or redundant. This not only inflates costs but also complicates the process ➡️➡️➡️

The year 2025 is shaping up to be a pivotal time in the realm of artificial intelligence. As we move forward, the emergence of agentic systems—autonomous AI agents capable of sophisticated reasoning and coordinated actions—will significantly transform various aspects of our lives. From enhancing enterprise workflows to improving everyday user experiences, these advancements are bound ➡️➡️➡️

As we look toward 2025, the landscape of artificial intelligence (AI) is evolving rapidly, particularly in how AI agents operate. Traditional AI workflows often fall short due to reliance on “single-step thinking,” which limits their ability to tackle complex, multi-part problems. To address this, we need to adopt new paradigms that embrace agentic AI workflows. ➡️➡️➡️

Building an Advanced PaperQA2 Research Agent with Google Gemini for Scientific Literature Analysis This guide will walk you through creating an advanced PaperQA2 AI Agent powered by Google’s Gemini model, specifically tailored for analyzing scientific literature. By following these steps, you will set up your environment in Google Colab or Notebook, configure the Gemini API, ➡️➡️➡️

Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, elevating the standards for tasks such as question answering and content generation. However, a significant challenge remains: the tendency of these models to produce inaccurate or misleading outputs, often referred to as “hallucination.” To mitigate this issue, Retrieval-Augmented Generation (RAG) frameworks have ➡️➡️➡️

Understanding the Mixture-of-Agents (MoA) Architecture The Mixture-of-Agents (MoA) architecture represents a significant advancement in the performance of large language models (LLMs). It addresses the challenges faced by traditional models, particularly in complex, open-ended tasks where accuracy and reasoning are paramount. By utilizing a layered structure of specialized agents, MoA enhances the capabilities of AI systems. ➡️➡️➡️

Understanding AI Agents in 2025 As we look ahead to 2025, the landscape of artificial intelligence is evolving rapidly, particularly in the realm of AI agents. These systems are designed to perceive, plan, and act autonomously within software environments, aiming to achieve specific goals with minimal human intervention. This article breaks down what AI agents ➡️➡️➡️

Understanding MCP-RL and ART Large language models (LLMs) are transforming how we interact with technology, and the Model Context Protocol (MCP) is at the forefront of this evolution. MCP provides a standardized way for LLMs to connect with various external systems, such as APIs and databases, without needing extensive custom coding. However, the challenge lies ➡️➡️➡️

Introduction to Alibaba’s Qwen Models Alibaba’s Qwen team has made waves in the AI landscape with the launch of two innovative small language models: Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507. Despite their relatively compact size, with 4 billion parameters each, these models demonstrate remarkable efficiency and performance across multiple tasks, making them suitable for use on standard consumer ➡️➡️➡️

Understanding the Target Audience The primary audience for VL-Cogito consists of AI researchers, technology business leaders, and educators keen on the advancements in multimodal reasoning and reinforcement learning. These individuals often face challenges when integrating diverse data sources, improving model accuracy, and addressing the limitations of existing AI systems. They are eager to deepen their ➡️➡️➡️

Introduction to GPT-5 OpenAI’s GPT-5 model has introduced several exciting capabilities that enhance its functionality and usability for developers. This guide will delve into these features, including the Verbosity parameter, Free-form Function Calling, Context-Free Grammar (CFG), and Minimal Reasoning. Each section will provide practical insights into how to leverage these new tools effectively. Installing the ➡️➡️➡️