LLM-based multi-agent (LLM-MA) systems enable multiple language model agents to collaborate on complex tasks by dividing responsibilities. These systems are used in robotics, finance, and coding but face challenges in communication and refinement. Text-based communication leads to long, unstructured exchanges, making it hard to track tasks, maintain structure, and recall past interactions. Refinement methods like… →
Large Language Models (LLMs) face significant challenges in complex reasoning tasks, despite the breakthrough advances achieved through Chain-of-Thought (CoT) prompting. The primary challenge lies in the computational overhead introduced by longer CoT sequences, which directly impacts inference latency and memory requirements. The autoregressive nature of LLM decoding means that as CoT sequences grow longer, there… →
Humans have an innate ability to process raw visual signals from the retina and develop a structured understanding of their surroundings, identifying objects and motion patterns. A major goal of machine learning is to uncover the underlying principles that enable such unsupervised human learning. One key hypothesis, the predictive feature principle, suggests that representations of… →
Large language models (LLMs) are limited by complex reasoning tasks that require multiple steps, domain-specific knowledge, or external tool integration. To address these challenges, researchers have explored ways to enhance LLM capabilities through external tool usage. By leveraging pre-built tools, AI systems can handle more intricate problem-solving scenarios, including real-world decision-making, multi-step reasoning, and specialized… →
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… →