In large language models (LLMs), processing extended input sequences demands significant computational and memory resources, leading to slower inference and higher hardware costs. The attention mechanism, a core component, further exacerbates these challenges due to its quadratic complexity relative to sequence length. Also, maintaining the previous context using a key-value (KV) cache results in high… →
ST-elevation myocardial infarction (STEMI) triggers a significant inflammatory response. Sweat may offer a novel, non-invasive medium for monitoring inflammation. In this prospective study, we characterized the inflammatory signatures in plasma and sweat collected from the skin surface of two patient groups: (1) 18 STEMI patients immediately following percutaneous coronary intervention (exposure) and (2) six patients… →
Androgenetic alopecia (AGA) is commonly treated with topical minoxidil, while platelet-rich plasma (PRP) and oral minoxidil offer alternative options. To compare the efficacy and safety of low-dose oral minoxidil (group 1) (G1), topical minoxidil (group 2) (G2), and PRP with topical minoxidil (group 3) (G3) in AGA. Seventy-five participants were randomly assigned to three treatment… →
AI has witnessed rapid advancements in NLP in recent years, yet many existing models still struggle to balance intuitive responses with deep, structured reasoning. While proficient in conversational fluency, traditional AI chat models often fail to meet when faced with complex logical queries requiring step-by-step analysis. On the other hand, models optimized for reasoning tend… →
AI chatbots create the illusion of having emotions, morals, or consciousness by generating natural conversations that seem human-like. Many users engage with AI for chat and companionship, reinforcing the false belief that it truly understands. This leads to serious risks. Users can over-rely on AI, provide sensitive data, or rely on it for advice beyond… →
Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate the performance of a larger teacher model. The idea is to enable efficient deployment without compromising performance. Understanding the principles behind distillation and how computational… →
CONCLUSIONS: The availability of robust evidence supporting or refuting the use of cervical traction as part of the management of cervical radiculopathy will enable optimisation of treatment. The results could lead to the drafting of evidence-based recommendations regarding the use of mechanical traction to treat cervical radiculopathy. →
Large Language Models (LLMs) have advanced significantly in natural language processing, yet reasoning remains a persistent challenge. While tasks such as mathematical problem-solving and code generation benefit from structured training data, broader reasoning tasks—like logical deduction, scientific inference, and symbolic reasoning—suffer from sparse and fragmented data. Traditional approaches, such as continual pretraining on code, often… →
Large language models (LLMs) have demonstrated exceptional problem-solving abilities, yet complex reasoning tasks—such as competition-level mathematics or intricate code generation—remain challenging. These tasks demand precise navigation through vast solution spaces and meticulous step-by-step deliberation. Existing methods, while improving accuracy, often suffer from high computational costs, rigid search strategies, and difficulty generalizing across diverse problems. In… →
Quantization is a crucial technique in deep learning for reducing computational costs and improving model efficiency. Large-scale language models demand significant processing power, which makes quantization essential for minimizing memory usage and enhancing inference speed. By converting high-precision weights to lower-bit formats such as int8, int4, or int2, quantization reduces storage requirements. However, standard techniques… →