Diffusion models have emerged as a crucial generative AI framework, excelling in tasks such as image synthesis, video generation, text-to-image translation, and molecular design. These models function through two stochastic processes: a forward process that incrementally adds noise to data, converting it into Gaussian noise, and a reverse process that reconstructs samples by learning to…
Transforming language models into effective red teamers is not without its challenges. Modern large language models have transformed the way we interact with technology, yet they still struggle with preventing the generation of harmful content. Efforts such as refusal training help these models deny risky requests, but even these safeguards can be bypassed with carefully…
Artificial intelligence in multi-agent environments has made significant strides, particularly in reinforcement learning. One of the core challenges in this domain is developing AI agents capable of communicating effectively through natural language. This is particularly critical in settings where each agent has only partial visibility of the environment, making knowledge-sharing essential for achieving collective goals.…
Recent discussions on AI safety increasingly link it to existential risks posed by advanced AI, suggesting that addressing safety inherently involves considering catastrophic scenarios. However, this perspective has drawbacks: it may exclude researchers with different approaches, mislead the public into thinking AI safety is solely about existential threats, and create resistance among skeptics. As AI…
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…
In large language models (LLMs), processing extended input sequences demands significant computational and memory resources, leading to slower inference and higher hardware costs. The attention mechanism, a core component, further exacerbates these challenges due to its quadratic complexity relative to sequence length. Also, maintaining the previous context using a key-value (KV) cache results in high…