Vladimir Dyachkov PhD

Scalable Generative Modeling: Meta AI’s Adjoint Sampling Scalable Generative Modeling: Meta AI’s Adjoint Sampling Understanding the Challenge of Data Scarcity Generative models have long depended on large, high-quality datasets to create samples that accurately reflect the data’s underlying distribution. However, in specialized fields like molecular modeling and physics, obtaining such data can be extremely difficult […] ➡️➡️➡️

Creating an AI Agent with Google ADK: A Practical Guide Creating an AI Agent with Google ADK: A Practical Guide The Agent Development Kit (ADK) is a powerful, open-source Python framework designed for developers to create, manage, and deploy multi-agent systems. Its flexible architecture makes it ideal for both simple and complex applications. This guide […] ➡️➡️➡️

Google AI Unveils MedGemma: Advanced Tools for Medical Text and Image Analysis At the recent Google I/O 2025, Google showcased MedGemma, a comprehensive suite of models tailored for understanding both medical text and images. Built on the Gemma 3 architecture, MedGemma provides developers with essential tools for developing healthcare applications that require intricate analysis of […] ➡️➡️➡️

NVIDIA Launches Cosmos-Reason1: Advancing AI in Physical Environments Introduction to Physical AI Artificial Intelligence (AI) has made remarkable progress in areas like language processing and code generation. However, applying these capabilities to real-world environments poses unique challenges. Physical AI is designed to address this issue by creating systems that can perceive, understand, and interact with […] ➡️➡️➡️

Enhancing Language Model Generalization Enhancing Language Model Generalization: Bridging the Gap Between In-Context Learning and Fine-Tuning Language models (LMs) have shown remarkable abilities in learning from context, especially when trained on vast amounts of internet text. This capability allows them to generalize effectively from just a few examples. However, fine-tuning these models for specific tasks […] ➡️➡️➡️

MemEngine: Enhancing Memory in AI Agents MemEngine: Enhancing Memory in AI Agents Researchers from Renmin University and Huawei have introduced MemEngine, a groundbreaking library designed to enhance memory systems in large language model (LLM)-based agents. This innovation addresses the growing need for efficient memory management in AI applications, enabling agents to perform complex tasks more […] ➡️➡️➡️

Meta’s KernelLLM: Transforming GPU Programming Meta’s KernelLLM: Transforming GPU Programming Overview of KernelLLM Meta has recently introduced KernelLLM, an advanced language model designed to streamline the process of developing GPU kernels. With 8 billion parameters, KernelLLM fine-tunes from Llama 3.1 Instruct and focuses on converting PyTorch modules into efficient Triton GPU kernels. This innovation aims […] ➡️➡️➡️

Efficient Fine-Tuning of Qwen3-14B Using Unsloth AI A Practical Guide to Fine-Tuning Qwen3-14B with Unsloth AI Introduction Fine-tuning large language models (LLMs) like Qwen3-14B can be resource-intensive, often requiring substantial time and memory. This can slow down experimentation and deployment. Unsloth AI offers a streamlined approach to fine-tuning these advanced models, reducing GPU memory usage […] ➡️➡️➡️

Google AI’s NotebookLM Mobile App: A Game Changer for Research Google AI’s NotebookLM Mobile App: A Game Changer for Research Introduction Google has made a significant advancement in AI with the release of the NotebookLM mobile application, now available for Android devices. This innovative app serves as a research assistant that users can access anytime, […] ➡️➡️➡️

Enhancing AI Evaluation with UAEval4RAG Enhancing AI Evaluation with UAEval4RAG Salesforce researchers have introduced a new framework called UAEval4RAG, designed to improve how we evaluate Retrieval-Augmented Generation (RAG) systems. This framework focuses on the systems’ ability to reject queries that cannot be answered, an aspect often neglected by traditional evaluation methods. Acknowledging this capability is […] ➡️➡️➡️

Agentic AI in Financial Services Agentic AI in Financial Services: Opportunities and Considerations Introduction to Agentic AI Agentic AI refers to advanced software systems capable of making autonomous decisions and planning over time. These systems are distinct from conventional automation tools and chatbots as they utilize planning, memory, and reasoning to perform dynamic tasks. According […] ➡️➡️➡️

Understanding AI Reasoning: Insights from Anthropic’s Recent Study Introduction to Chain-of-Thought Prompting Chain-of-thought (CoT) prompting has emerged as a method designed to clarify how large language models (LLMs) arrive at their conclusions. The idea is simple: when models explain their answers step-by-step, these steps should ideally reflect their actual reasoning. This is especially important in […] ➡️➡️➡️

Advancing Audio Question Answering with Omni-R1 Recent innovations in artificial intelligence demonstrate that reinforcement learning (RL) can greatly enhance the reasoning skills of large language models (LLMs). This article explores how Omni-R1 advances audio question answering by integrating text-driven reinforcement learning and auto-generated data. Understanding the Technology Audio LLMs are designed to process both audio […] ➡️➡️➡️

Cost-Effective Vector Search with Microsoft Azure Cosmos DB Microsoft’s Innovative Vector Search Solution Microsoft has developed a groundbreaking system that integrates vector search capabilities directly into Azure Cosmos DB. This advancement allows businesses to perform efficient searches on high-dimensional vector data, which is essential for applications like web search, AI assistants, and content recommendations. Understanding […] ➡️➡️➡️

Addressing Security Vulnerabilities in the Model Context Protocol (MCP) The Model Context Protocol (MCP) is revolutionizing how large language models engage with external tools and services. Designed for dynamic interactions, it introduces substantial efficiencies but also poses significant security risks. Identifying and mitigating these vulnerabilities is crucial for businesses leveraging AI technology. Key Vulnerabilities in […] ➡️➡️➡️