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​[[{“value”:” In today’s digital era, the way we work is rapidly evolving, yet many challenges persist. Conventional AI assistants and manual workflows struggle to keep pace with the complexity and volume of modern tasks. Professionals and businesses face repetitive manual processes, inefficient research methods, and… Read More »Meet Manus: A New AI Agent from China with Deep Research + Operator + Computer Use + Lovable + Memory Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have made significant strides in their post-training phase, like DeepSeek-R1, Kimi-K1.5, and OpenAI-o1, showing impressive reasoning capabilities. While DeepSeek-R1 provides open-source model weights, it withholds training code and dataset details, raising questions about scaling reasoning abilities to smaller models, optimal… Read More »Microsoft and Ubiquant Researchers Introduce Logic-RL: A Rule-based Reinforcement Learning Framework that Acquires R1-like Reasoning Patterns through Training on Logic Puzzles Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Deep learning models, having revolutionized areas of computer vision and natural language processing, become less efficient as they increase in complexity and are bound more by memory bandwidth than pure processing power. The latest GPUs struggle with tremendous bandwidth limitations as they are constantly… Read More »This AI Paper from MIT and UCL Introduces a Diagrammatic Approach for GPU-Aware Deep Learning Optimization Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” LLMs exhibit striking parallels to neural activity within the human language network, yet the specific linguistic properties that contribute to these brain-like representations remain unclear. Understanding the cognitive mechanisms that enable language comprehension and communication is a key objective in neuroscience. The brain’s language… Read More »Evaluating Brain Alignment in Large Language Models: Insights into Linguistic Competence and Neural Representations Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The landscape of generative AI and LLMs has experienced a remarkable leap forward with the launch of Mercury by the cutting-edge startup Inception Labs. Introducing the first-ever commercial-scale diffusion large language models (dLLMs), Inception labs promises a paradigm shift in speed, cost-efficiency, and intelligence… Read More »Inception Unveils Mercury: The First Commercial-Scale Diffusion Large Language Model Jean-marc Mommessin Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Researchers at The Ohio State University have introduced Finer-CAM, an innovative method that significantly improves the precision and interpretability of image explanations in fine-grained classification tasks. This advanced technique addresses key limitations of existing Class Activation Map (CAM) methods by explicitly highlighting subtle yet… Read More »Finer-CAM Revolutionizes AI Visual Explainability: Unlocking Precision in Fine-Grained Image Classification Jean-marc Mommessin Artificial Intelligence Category – MarkTechPost

​This tutorial is in two parts; they are: • Using DistilBart for Summarization • Improving the Summarization Process Let’s start with a fundamental implementation that demonstrates the key concepts of text summarization with DistilBart: import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM class TextSummarizer: def __init__(self,… Read More »Text Summarization with DistillBart Model Muhammad Asad Iqbal Khan MachineLearningMastery.com

​[[{“value”:” Large Language Models (LLMs) benefit significantly from reinforcement learning techniques, which enable iterative improvements by learning from rewards. However, training these models efficiently remains challenging, as they often require extensive datasets and human supervision to enhance their capabilities. Developing methods that allow LLMs to… Read More »Tufa Labs Introduced LADDER: A Recursive Learning Framework Enabling Large Language Models to Self-Improve without Human Intervention Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) are essential in fields that require contextual understanding and decision-making. However, their development and deployment come with substantial computational costs, which limits their scalability and accessibility. Researchers have optimized LLMs to improve efficiency, particularly fine-tuning processes, without sacrificing reasoning capabilities… Read More »This AI Paper Introduces a Parameter-Efficient Fine-Tuning Framework: LoRA, QLoRA, and Test-Time Scaling for Optimized LLM Performance Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In today’s rapidly evolving AI landscape, one persistent challenge is equipping language models with robust decision-making abilities that extend beyond single-turn interactions. Traditional large language models (LLMs) excel at generating coherent responses but often struggle with multi-step problem solving or interacting with dynamic environments.… Read More »CMU Researchers Introduce PAPRIKA: A Fine-Tuning Approach that Enables Language Models to Develop General Decision-Making Capabilities Not Confined to Particular Environment Asif Razzaq Artificial Intelligence Category – MarkTechPost