Subject: Conceptual Architecture for Graph-RAG: Mitigating "Lost in the Middle" Hello everyone! 👋 I would like to share a conceptual architecture aimed at solving the "Lost in the Middle" phenomenon and reducing GPU overhead in RAG systems. Instead of a standard single-vector semantic retrieval, the idea is to implement a "Multi-Directional Context Expansion" model. At the moment of extraction, the mechanism executes a multi-directional spatial scan of adjacent nodes (capturing the surrounding topology: above, below, left, right). Key advantages: • Contextual Integrity: Eliminates the need for expensive top-layer reranking. • Resource Efficiency: Drastically cuts down token consumption and GPU overhead. • Proactive Inference: Prepares the model for the next token step intuitively. I believe integrating this topological approach into standard vector-graph hybrids (Graph RAG) could significantly reduce contextual reasoning errors while optimizing infrastructure costs. I would love to get some feedback from the AI Research & Engineering team here. Who would be the best person to tag or discuss this with? Thanks!