Large language models (LLMs) have revolutionized various domains, including code completion, where artificial intelligence predicts and suggests code based on a developer’s previous inputs. This technology significantly enhances productivity, enabling developers to write code faster and with fewer errors. Despite the promise of LLMs, many models struggle with balancing speed and accuracy. Larger models often… →

Large language models (LLMs) have revolutionized the field of artificial intelligence by performing a wide range of tasks across different domains. These models are expected to work seamlessly in multiple languages, solving complex problems while ensuring safety. However, the challenge lies in maintaining safety without compromising performance, especially in multilingual settings. As AI technologies become… →

Vision-Language-Action Models (VLA) for robotics are trained by combining large language models with vision encoders and then fine-tuning them on various robot datasets; this allows generalization to new instructions, unseen objects, and distribution shifts. However, various real-world robot datasets mostly require human control, which makes scaling difficult. On the other hand, Internet video data offers… →

A primary feature of sophisticated language models is In-Context Learning (ICL), which allows the model to produce answers based on input instances without being specifically instructed on how to complete the task. In ICL, a few examples that show the intended behavior or pattern are shown to the model, which then applies this knowledge to… →

The discovery of new materials is crucial to addressing pressing global challenges such as climate change and advancements in next-generation computing. However, existing computational and experimental approaches face significant limitations in efficiently exploring the vast chemical space. While AI has emerged as a powerful tool for materials discovery, the lack of publicly available data and… →

The growing reliance on large language models for coding support poses a significant problem: how best to assess real-world impact on programmer productivity? Current approaches, such as static bench-marking based on datasets such as HumanEval, measure the correctness of the code but cannot capture the dynamic, human-in-the-loop interaction of real programming activity. With LLMs increasingly… →

In the rapidly evolving world of AI, challenges related to scalability, performance, and accessibility remain central to the efforts of research communities and open-source advocates. Issues such as the computational demands of large-scale models, the lack of diverse model sizes for different use cases, and the need to balance accuracy with efficiency are critical obstacles.… →