Supervised fine-tuning (SFT) is the standard training paradigm for large language models (LLMs) and graphic user interface (GUI) agents. However, SFT demands high-quality labeled datasets, resulting ...

Time series analysis faces significant hurdles in data availability, quality, and diversity, critical factors in developing effective foundation models. Real-world datasets often fall short due to ...

Monocular depth estimation involves predicting scene depth from a single RGB image—a fundamental task in computer vision with wide-ranging applications, including augmented reality, robotics, and 3D ...

Autoregressive visual generation models have emerged as a groundbreaking approach to image synthesis, drawing inspiration from language model token prediction mechanisms. These innovative models ...

Large Language Models (LLMs) are becoming integral to modern technology, driving agentic systems that interact dynamically with external environments. Despite their impressive capabilities, LLMs are ...

Large Vision-Language Models (LVLMs) have made significant strides in recent years, yet several key limitations persist. One major challenge is aligning these models effectively with human ...

In this guide, you will learn how to deploy a machine learning model as an API using FastAPI. We will create an API that predicts the species of a penguin based on ...

Compression is a cornerstone of computational intelligence, deeply rooted in the theory of Kolmogorov complexity, which defines the minimal program needed to reproduce a given sequence. Unlike ...

Large language models are powering a new wave of digital agents to handle sophisticated web-based tasks. These agents are expected to interpret user instructions, navigate interfaces, and execute ...

Innovative frameworks that simplify complex interactions with large language models have fundamentally transformed the landscape of generative AI development in Python. PydanticAI emerges as a robust ...

Despite the growing interest in Multi-Agent Systems (MAS), where multiple LLM-based agents collaborate on complex tasks, their performance gains remain limited compared to single-agent frameworks.

Artificial intelligence (AI) has made significant strides in recent years, yet challenges persist in achieving efficient, cost-effective, and high-performance models. Developing large language models ...