Procedural World with AI NPCs

🧠 Project Pitch: Temporal Multimodal RAG for Lifelong Contextual Understanding ❓ Problem Modern Retrieval-Augmented Generation (RAG) systems excel at processing static text, but they lack context over time, lose multimodal integration, and struggle to link events across different types of data (text, image, audio, video). In critical domains like medicine, law, or security, this limits their ability to reason causally, temporally, or contextually across cases. 💡 Solution We’re developing a Temporal-Aware Multimodal RAG system — an AI memory architecture that models real-time evolving knowledge much like a human brain: “Every memory is a node; every node connects causally and temporally to others — across all senses.” Our system supports: Text, Image, Audio, and Video embeddings Scene-level segmentation for video and audio Causal-temporal linkage between memory nodes OLAP-enhanced SQL backend for flexible and explainable reasoning Unlearning capability, allowing dynamic memory rewriting without full retraining