​[[{“value”:” Deploying Deep Neural Networks (DNNs) on edge devices, such as smartphones and autonomous vehicles, remains a significant challenge due to their computationally intensive nature.  Most existing pruning algorithms struggle to balance high compression rates and inference accuracy and have to be compatible with commercial… Read More »AutoSculpt: A Pattern-based Automated Pruning Framework Designed to Enhance Efficiency and Accuracy by Leveraging Graph Learning and Deep Reinforcement Learning Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” A direct correlation exists between an LLM’s training corpus quality and its capabilities. Consequently, researchers have invested a great deal of effort into curating extensive, high-quality datasets, which, at present, are achievable with craftful human annotations. Man-made datasets, however, have one downside: their reliance… Read More »B-STAR: A Self-Taught AI Reasoning Framework for LLMs Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Researchers are focusing increasingly on creating systems that can handle multi-modal data exploration, which combines structured and unstructured data. This involves analyzing text, images, videos, and databases to answer complex queries. These capabilities are crucial in healthcare, where medical professionals interact with patient records,… Read More »This AI Paper Introduces XMODE: An Explainable Multi-Modal Data Exploration System Powered by LLMs for Enhanced Accuracy and Efficiency Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” LLMs have revolutionized software development by automating coding tasks and bridging the natural language and programming gap. While highly effective for general-purpose programming, they struggle with specialized domains like High-Performance Computing (HPC), particularly in generating parallel code. This limitation arises from the scarcity of… Read More »Advancing Parallel Programming with HPC-INSTRUCT: Optimizing Code LLMs for High-Performance Computing Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have shown significant potential in reasoning tasks, using methods like Chain-of-Thought (CoT) to break down complex problems into manageable steps. However, this capability comes with challenges. CoT prompts often increase token usage, leading to higher computational costs and energy consumption.… Read More »This AI Paper Proposes TALE: An AI Framework that Reduces Token Redundancy in Chain-of-Thought (CoT) Reasoning by Incorporating Token Budget Awareness Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The development of Transformer models has significantly advanced artificial intelligence, delivering remarkable performance across diverse tasks. However, these advancements often come with steep computational requirements, presenting challenges in scalability and efficiency. Sparsely activated Mixture-of-Experts (MoE) architectures provide a promising solution, enabling increased model capacity… Read More »Researchers from Tsinghua University Propose ReMoE: A Fully Differentiable MoE Architecture with ReLU Routing Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential equations (PDEs). Unlike traditional neural network tasks, these mappings operate in infinite-dimensional spaces, making them particularly suitable… Read More »NeuralOperator: A New Python Library for Learning Neural Operators in PyTorch Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Agentic AI systems have revolutionized industries by enabling complex workflows through specialized agents working in collaboration. These systems streamline operations, automate decision-making, and enhance overall efficiency across various domains, including market research, healthcare, and enterprise management. However, their optimization remains a persistent challenge, as… Read More »aiXplain Introduces a Multi-AI Agent Autonomous Framework for Optimizing Agentic AI Systems Across Diverse Industries and Applications Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Hypernetworks have gained attention for their ability to efficiently adapt large models or train generative models of neural representations. Despite their effectiveness, training hyper networks are often labor-intensive, requiring precomputed optimized weights for each data sample. This reliance on ground truth weights necessitates significant… Read More »Hypernetwork Fields: Efficient Gradient-Driven Training for Scalable Neural Network Optimization Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Formal mathematical reasoning represents a significant frontier in artificial intelligence, addressing fundamental logic, computation, and problem-solving challenges. This field focuses on enabling machines to handle abstract mathematical reasoning with precision and rigor, extending AI’s applications in science, engineering, and other quantitative domains. Unlike natural… Read More »This AI Paper Explores How Formal Systems Could Revolutionize Math LLMs Nikhil Artificial Intelligence Category – MarkTechPost