I`m not native English speaking so i`m - but i do not care of opinions - here is some of the sequence solutions: Subject: Architectural Proposal: Mitigating RAG Context Loss via Multi-Directional Topology (Graph-based Context Expansion) I would like to propose a conceptual architecture aimed at solving the "Lost in the Middle" phenomenon and reducing computational overhead in Retrieval-Augmented Generation (RAG) systems. Current RAG pipelines typically deploy a single vector retrieval mechanism ("one-way elevator"), fetching isolated data points based purely on semantic similarity. This often breaks the continuity of the logic, leading to hallucinations due to missing peripheral arguments and counterpoints. Proposed Solution: The "Multi-Directional Context Expansion" Model Instead of fetching an isolated vector, the retrieval mechanism should dynamically execute a multi-directional spatial scan (adjacent nodes) at the moment of extraction. Think of this as deploying local "tentacles" to capture the surrounding topology (above, below, left, right) of the targeted data piece. Key Benefits: 1. Contextual Integrity: The system retrieves data paired with its local causal history, eliminating the need for expensive, compute-heavy top-layer reranking. 2. Computational & Energy Efficiency: By ensuring local context validity during the extraction phase, the system reduces the need for continuous global context window alignment, drastically cutting down token consumption and GPU overhead. 3. Proactive Inference: Mapping the immediate surroundings of a data node allows the attention mechanism to predict the logical trajectory of the sequence, preparing the model for the next token step intuitively. I believe integrating this topological approach into standard vector-graph hybrids (Graph RAG) could reduce contextual reasoning errors significantly while optimizing infrastructure costs. I would be glad to share more details on this concept. [Eugeen Iliev & AI/II]