Code retrieval has become essential for developers in modern software development, enabling efficient access to relevant code snippets and documentation. Unlike traditional text retrieval, which effectively handles natural language queries, code retrieval must address unique challenges, such as programming languages’ structural variations, dependencies, and contextual relevance. With tools like GitHub Copilot gaining popularity, advanced code… →

The development of VLMs in the biomedical domain faces challenges due to the lack of large-scale, annotated, and publicly accessible multimodal datasets across diverse fields. While datasets have been constructed from biomedical literature, such as PubMed, they often focus narrowly on domains like radiology and pathology, neglecting complementary areas such as molecular biology and pharmacogenomics… →

Vision-language models (VLMs) represent an advanced field within artificial intelligence, integrating computer vision and natural language processing to handle multimodal data. These models allow systems to simultaneously understand and process images and text, enabling applications like medical imaging, automated systems, and digital content analysis. Their ability to bridge the gap between visual & textual data… →

Humans possess an extraordinary ability to localize sound sources and interpret their environment using auditory cues, a phenomenon termed spatial hearing. This capability enables tasks such as identifying speakers in noisy settings or navigating complex environments. Emulating such auditory spatial perception is crucial for enhancing the immersive experience in technologies like augmented reality (AR) and… →

The rapid advancement and widespread adoption of generative AI systems across various domains have increased the critical importance of AI red teaming for evaluating technology safety and security. While AI red teaming aims to evaluate end-to-end systems by simulating real-world attacks, current methodologies face significant challenges in effectiveness and implementation. The complexity of modern AI… →

Large Language Models (LLMs) have become essential tools in software development, offering capabilities such as generating code snippets, automating unit tests, and debugging. However, these models often fall short in producing code that is not only functionally correct but also efficient in runtime. Overlooking runtime efficiency can lead to software that performs poorly, increases operational… →

Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements in scaling generator models have been substantial, tokenizers—primarily based on convolutional neural networks (CNNs)—have received comparatively less attention. This raises questions about how scaling tokenizers might improve reconstruction accuracy and generative tasks. Challenges include architectural… →

CrewAI is an innovative platform that transforms how AI agents collaborate to solve complex problems. As an orchestration framework, it empowers users to assemble and manage teams of specialized AI agents, each tailored to perform specific tasks within an organized workflow. Just as a well-run organization delegates roles and responsibilities among its departments, CrewAI assigns… →

Domains like social media analysis, e-commerce, and healthcare data management require querying through large chunks of structured and unstructured databases. In this modern world, there has been an ever-increasing requirement for the same in many other domains. However, current systems have been proven inefficient due to their inability to tackle the diverse obstacles presented when… →