Compute Express Link (CXL) emerges as an innovative technological solution addressing critical memory wall challenges in modern computing infrastructures. The interconnect technology presents a comprehensive approach to overcoming existing memory architecture limitations, offering high bandwidth density and a standardized interface for memory expansion and pooling. CXL’s innovative design has attracted substantial attention from both industrial… →
Recent advances in natural language processing (NLP), led by large-scale pre-trained models such as GPT-3 and BERT, have transformed text generation and sentiment analysis tasks. These models’ ability to adapt to various applications with less data has contributed to their popularity in sensitive industries such as healthcare and finance. However, implementing these models creates significant… →
The development of effective AI models is crucial in deep learning research, but finding optimal model architectures remains challenging and costly. Traditional manual and automated approaches often fail to expand design possibilities beyond basic architectures like Transformers or hybrids, and the high cost of exploring a comprehensive search space limits model improvement. Manual optimization demands… →
In today’s world, large language models have shown great performance on various tasks and demonstrated different reasoning capabilities. This is important for advancing Artificial General Intelligence (AGI) and its use in robotics and navigation. Spatial reasoning includes quantitative aspects (e.g., distances, angles) and qualitative aspects (e.g., relative positions like “near” or “inside”). While humans excel… →
Artificial intelligence has been progressively transforming with domain-specific models that excel in handling tasks within specialized fields such as mathematics, healthcare, and coding. These models are designed to enhance task performance and resource efficiency. However, integrating such specialized models into a cohesive and versatile framework remains a substantial challenge. Researchers are actively seeking innovative solutions… →
Universities face intense global competition in the contemporary academic landscape, with institutional rankings increasingly tied to the United Nations’ Sustainable Development Goals (SDGs) as a critical social impact assessment benchmark. These rankings significantly influence crucial institutional parameters such as funding opportunities, international reputation, and student recruitment strategies. The current methodological approach to tracking SDG-related research… →
Building large language model (LLM)-powered applications for real-world production scenarios is challenging. Developers often face issues such as inconsistent responses from models, difficulties in ensuring robustness, and a lack of strong type safety. When building applications that leverage LLMs, the goal is to provide reliable, accurate, and contextually appropriate outputs to users, which requires consistency,… →
The static knowledge base and hallucination-creating inaccuracy or fabrication of information are two common issues with large language models (LLMs). The parametric knowledge within LLMs is inherently static, making it challenging to provide up-to-date information in real-time scenarios. Retrieval-augmented generation (RAG) addresses the problem of integrating external, real-time information to enhance accuracy and relevance. However,… →
The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. Many existing datasets are limited, often covering only small classes of physical behaviors. This lack of comprehensive data makes it challenging to develop effective surrogate models… →