In this tutorial, we’ll learn how to create a custom tokenizer using the tiktoken library. The process involves loading a pre-trained tokenizer model, defining both base and special tokens, initializing the tokenizer with a specific regular expression for token splitting, and testing its functionality by encoding and decoding some sample text. This setup is essential… →

Large Language Models (LLMs) have shown exceptional capabilities in complex reasoning tasks through recent advancements in scaling and specialized training approaches. While models like OpenAI o1 and DeepSeek R1 have set new benchmarks in addressing reasoning problems, a significant disparity exists in their performance across different languages. The dominance of English and Chinese in training… →

Graph generation is a complex problem that involves constructing structured, non-Euclidean representations while maintaining meaningful relationships between entities.  Most current methods fail to capture higher-order interactions, like motifs and simplicial complexes, required for molecular modeling, social network analysis, and protein design applications. Diffusion-based methods, first developed for image synthesis, have been popularized widely in the… →

After the advent of LLMs, AI Research has focused solely on the development of powerful models day by day. These cutting-edge new models improve users’ experience across various reasoning, content generation tasks, etc. However, trust in the results and the underlying reasoning used by these models have recently been in the spotlight. In developing these… →

Adapting large language models for specialized domains remains challenging, especially in fields requiring spatial reasoning and structured problem-solving, even though they specialize in complex reasoning. Semiconductor layout design is a prime example, where AI tools must interpret geometric constraints and ensure precise component placement. Researchers are developing advanced AI architectures to enhance LLMs’ ability to… →