Artificial intelligence systems often struggle with retaining meaningful context over extended interactions. This limitation poses challenges for applications such as chatbots and virtual assistants, where maintaining a coherent conversation thread is essential. Most traditional AI models operate in a stateless manner, focusing solely on immediate inputs without considering the continuity of prior exchanges. This lack… →

Developments in simulating particulate flows have significantly impacted industries ranging from mining to pharmaceuticals. Particulate systems consist of granular materials interacting with each other and surrounding fluids, and their accurate modeling is critical for optimizing processes. However, traditional numerical methods like the Discrete Element Method (DEM) face substantial computational limitations. These methods track particle movements… →

Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse applications, but their widespread adoption faces significant challenges. The primary concern stems from training datasets that contain varied, unfocused, and potentially harmful content, including malicious code and cyberattack-related information. This creates a critical need to align LLM outputs with specific user requirements while preventing misuse.… →

Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved state-of-the-art results in this area, they require large amounts of labeled data, which is costly and time-consuming. Active learning helps optimize this process by selecting the most informative unlabeled samples for annotation, reducing the labeling effort. However, most… →

Model efficiency is important in the age of large language and vision models, but they face significant efficiency challenges in real-world deployments. Critical metrics such as training compute requirements, inference latency, and memory footprint impact deployment costs and system responsiveness. These constraints often limit the practical implementation of high-quality models in production environments. The need… →

Data visualization is a powerful technique that transforms complex data into easily understandable visual representations. Let us explore how data visualization can help with graphs. Applying data visualization to graphs allows us to examine intricate relationships between entities, identify patterns, and uncover insights that might be hidden within the data. By visually mapping nodes and… →

A significant challenge in the field of artificial intelligence is to facilitate large language models (LLMs) to generate 3D meshes from text descriptions directly. Conventional techniques restrict LLMs from operating as text-based components and remove multimodal workflows that combine textual and 3D content creation. Most of the existing frameworks require additional architectures or massive computational… →

Instruction-tuned large language models (LLMs) have redefined natural language processing (NLP), offering significant improvements in generating coherent, context-aware responses. However, a pressing challenge persists—access to high-quality, diverse, and task-specific instruction-response datasets. Traditional instruction-tuning approaches often depend on curated datasets that are costly and time-intensive to develop. Moreover, such datasets may lack the breadth and depth… →