Retrieval-Augmented Generation (RAG) systems generally rely on dense embedding models that map queries and documents into fixed-dimensional vector spaces. While this approach has become the default for many AI applications, a recent research from Google DeepMind team explains a fundamental architectural limitation that cannot be solved by larger models or better training alone. What Is […] The post Google DeepMind Finds a Fundamental Bug in RAG: Embedding Limits Break Retrieval at Scale app...| MarkTechPost
Introduction Understanding how the brain builds internal representations of the visual world is one of the most fascinating challenges in neuroscience. Over the past decade, deep learning has reshaped computer vision, producing neural networks that not only perform at human-level accuracy on recognition tasks but also seem to process information in ways that resemble our […] The post AI and the Brain: How DINOv3 Models Reveal Insights into Human Visual Processing appeared first on MarkTechP...| MarkTechPost
Introduction Vision Language Models (VLMs) allow both text inputs and visual understanding. However, image resolution is crucial for VLM performance for processing text and chart-rich data. Increasing image resolution creates significant challenges. First, pretrained vision encoders often struggle with high-resolution images due to inefficient pretraining requirements. Running inference on high-resolution images increases computational costs and […] The post Apple Released FastVLM: A Novel ...| MarkTechPost
Graph-R1, an advanced agentic GraphRAG framework using hypergraph knowledge and reinforcement learning for accurate, efficient QA| MarkTechPost
ByteDance Research Releases DAPO: A Fully Open-Sourced LLM Reinforcement Learning System at Scale| MarkTechPost
s1: A Simple Yet Powerful Test-Time Scaling Approach for LLMs| MarkTechPost