OpenAI’s GPT-4o represents a new milestone in multimodal AI: a single model capable of generating fluent text and high-quality images in the same output sequence. Unlike previous systems (e.g., ...

Enterprises increasingly adopt agentic frameworks to build intelligent systems capable of performing complex tasks by chaining tools, models, and memory components. However, as organizations build ...

Reinforcement Learning RL has become a widely used post-training method for LLMs, enhancing capabilities like human alignment, long-term reasoning, and adaptability. A major challenge, however, is ...

In today’s information-rich digital landscape, navigating extensive web content can be overwhelming. Whether you’re researching for a project, studying complex material, or trying to extract specific ...

While the outputs of large language models (LLMs) appear coherent and useful, the underlying mechanisms guiding these behaviors remain largely unknown. As these models are increasingly deployed in ...

The future of robotics has advanced significantly. For many years, there have been expectations of human-like robots that can navigate our environments, perform complex tasks, and work alongside ...

GenSpark Super Agent (often just called GenSpark) is a new general-purpose AI agent designed to autonomously handle complex tasks across domains. Unlike a simple chatbot or script, GenSpark can “think ...

The Model Context Protocol (MCP) is an open standard (open-sourced by Anthropic) that defines a unified way to connect AI assistants (LLMs) with external data sources and tools. Think of MCP as a ...

Optical Character Recognition (OCR) has long been a cornerstone of document digitization, enabling the transformation of printed text into machine-readable formats. However, traditional OCR systems ...

Large Multimodal Models (LMMs) have demonstrated remarkable capabilities when trained on extensive visual-text paired data, advancing multimodal understanding tasks significantly. However, these ...

As LLMs scale, their computational and bandwidth demands increase significantly, posing challenges for AI training infrastructure. Following scaling laws, LLMs improve comprehension, reasoning, and ...

Deploying LLMs presents challenges, particularly in optimizing efficiency, managing computational costs, and ensuring high-quality performance. LLM routing has emerged as a strategic solution to these ...