The capability of multimodal large language models (MLLMs) to enable complex long-chain reasoning that incorporates text and vision raises an even greater barrier in the realm of artificial intelligence. While text-centric reasoning tasks are being gradually advanced, multimodal tasks add additional challenges rooted in the lack of rich, comprehensive reasoning datasets and efficient training strategies.… →

Filtering, scanning, and updating data are important operations in databases, and many data structures are used to perform these operations. In real-world situations, it’s important to manage multidimensional data, and the Kd-tree and its variations are popular structures used for this purpose. Various research studies have focused on improving data structures by learning the distribution… →

Phase-field models serve as a crucial mesoscale simulation method, bridging atomic-scale models and macroscopic phenomena by describing microstructural evolution and phase transformations. These models extract local free energy density information from lower-scale simulations and use it to predict larger-scale material behavior. Phase-field methods are widely applied in processes such as grain growth, crack propagation, dendrite… →

Transformer architectures have revolutionized Natural Language Processing (NLP), enabling significant language understanding and generation progress. Large Language Models (LLMs), which rely on these architectures, have achieved remarkable performance across various applications such as conversational systems, content creation, and summarization. However, the efficiency of LLMs in real-world deployment remains a challenge due to their substantial resource… →

Speech recognition technology has made significant progress, with advancements in AI improving accessibility and accuracy. However, it still faces challenges, particularly in understanding spoken entities like names, places, and specific terminology. The issue is not only about converting speech to text accurately but also about extracting meaningful context in real-time. Current systems often require separate… →

The field of structured generation has become important with the rise of LLMs. These models, capable of generating human-like text, are now tasked with producing outputs that follow rigid formats such as JSON, SQL, and other domain-specific languages. Applications like code generation, robotic control, and structured querying depend heavily on these capabilities. However, ensuring that… →

In an era of information overload, advancing AI requires not just innovative technologies but smarter approaches to data processing and understanding. Meet CircleMind, an AI startup reimagining Retrieval Augmented Generation (RAG) by using knowledge graphs and the established PageRank algorithm. Funded by Y Combinator, CircleMind aims to improve how large language models (LLMs) understand and… →

Despite the success of Vision Transformers (ViTs) in tasks like image classification and generation, they face significant challenges in handling abstract tasks involving relationships between objects. One key limitation is their difficulty in accurately performing visual relational tasks, such as determining if two objects are the same or different. Relational reasoning, which requires understanding spatial… →

Strategic planning in artificial intelligence has reached significant milestones, especially in achieving superhuman performance in complex games like Go. Large Language Models (LLMs) integrated with advanced planning algorithms have shown remarkable improvements in complex reasoning tasks. However,  several critical challenges emerge when these capabilities are applied to web-based environments for executing complex tasks across diverse… →