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 ...

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 ...

AI chatbots create the illusion of having emotions, morals, or consciousness by generating natural conversations that seem human-like. Many users engage with AI for chat and companionship, reinforcing ...

Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate ...

Most modern visualization authoring tools like Charticulator, Data Illustrator, and Lyra, and libraries like ggplot2, and VegaLite expect tidy data, where every variable to be visualized is a column ...

In this tutorial, we will build an advanced AI-powered news agent that can search the web for the latest news on a given topic and summarize the results. This agent follows a structured workflow: To ...

Large language model (LLM)–based AI companions have evolved from simple chatbots into entities that users perceive as friends, partners, or even family members. Yet, despite their human-like ...

The Open O1 project is a groundbreaking initiative aimed at matching the powerful capabilities of proprietary models, particularly OpenAI’s O1, through an open-source approach. By leveraging advanced ...

Competitive programming has long served as a benchmark for assessing problem-solving and coding skills. These challenges require advanced computational thinking, efficient algorithms, and precise ...

Reasoning tasks are yet a big challenge for most of the language models. Instilling a reasoning aptitude in models, particularly for programming and mathematical applications that require solid ...

Human-robot collaboration focuses on developing intelligent systems working alongside humans in dynamic environments. Researchers aim to build robots capable of understanding and executing natural ...

The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable ...