Large language models (LLMs) have shown remarkable advancements in reasoning capabilities in solving complex tasks. While models like OpenAI’s o1 and DeepSeek’s R1 have significantly improved challenging reasoning benchmarks such as competition math, competitive coding, and GPQA, critical limitations remain in evaluating their true reasoning potential. The current reasoning datasets focus on problem-solving tasks but… →
Modern vision-language models have transformed how we process visual data, yet they often fall short when it comes to fine-grained localization and dense feature extraction. Many traditional models focus on high-level semantic understanding and zero-shot classification but struggle with detailed spatial reasoning. These limitations can impact applications that require precise localization, such as document analysis… →
Organizations face significant challenges when deploying LLMs in today’s technology landscape. The primary issues include managing the enormous computational demands required to process high volumes of data, achieving low latency, and ensuring optimal balance between CPU-intensive tasks, such as scheduling and memory allocation, and GPU-intensive computations. Repeatedly processing similar inputs further compounds the inefficiencies in… →
CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, prefrontal anodal tDCS induced small- to medium-sized effects in youths with ADHD, with only mild and transient adverse events. The optimized multichannel montage chosen to target the lDLPFC, however, decreased working memory performance. This unexpected finding stresses the importance of incorporating insights from basic research on the… →
CONCLUSIONS: The improvement effects of nutrition supplementation plus exercise were superior to those of exercise alone, and the improvement occurred earlier. Nutrition supplementation plus exercise would be a more efficient strategy for knee OA. →
Large Language models (LLMs) operate by predicting the next token based on input data, yet their performance suggests they process information beyond mere token-level predictions. This raises questions about whether LLMs engage in implicit planning before generating complete responses. Understanding this phenomenon can lead to more transparent AI systems, improving efficiency and making output generation… →
While LLMs have shown remarkable advancements in general-purpose applications, their development for specialized fields like medicine remains limited. The complexity of medical knowledge and the scarcity of high-quality, domain-specific data make creating highly efficient medical LLMs challenging. Although models like GPT-4 and DeepseekR1 have demonstrated impressive capabilities across industries, their adaptation to the medical domain… →
Evaluation of the proarrhythmic potential of imetelstat, a novel oligonucleotide telomerase inhibitor, in nonclinical and clinical studies is presented. In vitro, imetelstat sodium ≤ 750 μg/mL and negative (vehicle) and positive (cisapride) controls were evaluated for hERG channel current inhibition. In vivo, cynomolgus monkeys received a single vehicle control or imetelstat sodium (5 mg/kg [2-h… →