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… →
Large Language Models (LLMs) have gained significant importance as productivity tools, with open-source models increasingly matching the performance of their closed-source counterparts. These models operate through Next Token Prediction, where tokens are predicted in sequence when computing attention is between each token and its predecessors. Key-value (KV) pairs are cached to prevent redundant calculations and… →
Most modern visualization authoring tools like Charticulator, Data Illustrator, and Lyra, and libraries like ggplot2, and VegaLite expect tidy data, where every variable to be visualized is a column and each observation is a row. When the input data is in a tidy format, authors simply need to bind data columns to visual channels, otherwise,… →
Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance.… →
CONCLUSIONS AND RELEVANCE: In this nonrandomized clinical trial, ED vestibular therapy was feasibly delivered to patients presenting to the ED with undifferentiated dizziness symptoms. For participants receiving vestibular therapy the findings for dizziness-related disability over 3 months were not statistically significant, pointing to the need for a fully powered randomized clinical trial. →
This study aimed to evaluate the clinical efficacy of picosecond laser therapy combined with the Shumin Star in treating melasma and to explore the role of skin barrier function indicators in the assessment of this treatment process. Ninety patients with melasma were randomly divided into a study group and a control group. The study group… →
We’re excited to announce several key updates to the Perception Challenge for Bin-Picking aka the Bin-Picking Challenge (BPC)! Phase 1 Submissions Open On February 14th! Get ready to showcase your solutions! Phase 1 submissions will officially open on Friday, February 14th. Please follow the detailed submission instructions available on the challenge page. Make sure to… →
In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning capabilities. However, this progress comes with a significant caveat: the inference process—generating responses one token at a time—remains a computational bottleneck. As LLMs grow in size and complexity, the latency and energy demands… →
LLMs have demonstrated exceptional capabilities, but their substantial computational demands pose significant challenges for large-scale deployment. While previous studies indicate that intermediate layers in deep neural networks can be reordered or removed without severely impacting performance, these insights have not been systematically leveraged to reduce inference costs. Given the rapid expansion of LLMs, which often… →