Recent advancements in RL for LLMs, such as DeepSeek R1, have demonstrated that even simple question-answering tasks can significantly enhance reasoning capabilities. Traditional RL approaches for LLMs often rely on single-turn tasks, where a model is rewarded based on the correctness of a single response. However, these methods suffer from sparse rewards and fail to… →
Large reasoning models (LRMs) employ a deliberate, step-by-step thought process before arriving at a solution, making them suitable for complex tasks requiring logical accuracy. Unlike earlier techniques that relied on brief chain-of-thought reasoning, LRMs integrate intermediate verification steps, ensuring each stage contributes meaningfully toward the final answer. This structured reasoning approach is increasingly vital as… →
Autofocus plays a crucial role in imaging systems, ensuring that captured images and video frames are sharp and well-defined. In various applications, such as medical imaging, surveillance, and photography, selecting the sharpest frame from a sequence can significantly enhance the quality of analysis and presentation. In this article, we explore various focus measurement operators using… →
CONCLUSION: The regulation of the endometrial immune environment emerges as one of the leading innovative strategies to facilitate embryo implantation and enhance the overall performance of assisted reproductive therapies (ART). Based on these findings, endometrial immune profiling could become an essential part of routine ART practice. →
BACKGROUND: The management of locally advanced prostate cancer (PCa) and oligometastatic prostate cancer (OMPCa) remains a clinical challenge. The heterogeneous nature of PCa prompts a need for precision treatment. This study aims to verify whether genomic biomarker-guided neoadjuvant therapy for locally advanced PCa and OMPCa can result in an improvement in the pathological responses and… →
Understanding videos with AI requires handling sequences of images efficiently. A major challenge in current video-based AI models is their inability to process videos as a continuous flow, missing important motion details and disrupting continuity. This lack of temporal modeling prevents tracing changes; therefore, events and interactions are partially unknown. Long videos also make the… →
Reasoning language models have demonstrated the ability to enhance performance by generating longer chain-of-thought sequences during inference, effectively leveraging increased computation. However, a major limitation is the lack of control over reasoning length, making it difficult to allocate computational resources efficiently. In some cases, models generate excessively long outputs, wasting compute, while in others, they… →