The core idea of Multimodal Large Language Models (MLLMs) is to create models that can combine the richness of visual content with the logic of language. However, despite advances in this field, many models struggle to connect the two domains…
Many websites lack accessible and cost-effective ways to integrate natural language interfaces, making it difficult for users to interact with site content through conversational AI. Existing solutions often depend on centralized, proprietary services or require significant technical expertise, limiting scalability…
In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency…
LLMs have shown impressive capabilities across various programming tasks, yet their potential for program optimization has not been fully explored. While some recent efforts have used LLMs to enhance performance in languages like C++ and Python, the broader application of…
As businesses increasingly integrate AI assistants, assessing how effectively these systems perform real-world tasks, particularly through voice-based interactions, is essential. Existing evaluation methods concentrate on broad conversational skills or limited, task-specific tool usage. However, these benchmarks fall short when measuring…
A prominent area of exploration involves enabling large language models (LLMs) to function collaboratively. Multi-agent systems powered by LLMs are now being examined for their potential to coordinate challenging problems by splitting tasks and working simultaneously. This direction has gained…
In this tutorial, we demonstrated how Microsoft’s AutoGen framework empowers developers to orchestrate complex, multi-agent workflows with minimal code. By leveraging AutoGen’s RoundRobinGroupChat and TeamTool abstractions, you can seamlessly assemble specialist assistants, such as Researchers, FactCheckers, Critics, Summarizers, and Editors,…
The effectiveness of language models relies on their ability to simulate human-like step-by-step deduction. However, these reasoning sequences are resource-intensive and can be wasteful for simple questions that do not require elaborate computation. This lack of awareness regarding the complexity…
Recent advances in long-context (LC) modeling have unlocked new capabilities for LLMs and large vision-language models (LVLMs). Long-context vision–language models (LCVLMs) show an important step forward by enabling LVLMs to process hundreds of images and thousands of interleaved text tokens…
Modern web usage spans many digital interactions, from filling out forms and managing accounts to executing data queries and navigating complex dashboards. Despite the web being deeply intertwined with productivity and work processes, many of these actions still demand repetitive…
