DREAMWEAVE: Layered Memory Intelligence Framework

DREAMWEAVE is a cognitive memory framework and layered retrieval middleware designed to transform how AI systems store, organize, retrieve, and reason over knowledge. Unlike traditional RAG systems that rely on flat vector similarity search, DREAMWEAVE introduces a multi-layered memory architecture inspired by neuroscience, structural reasoning, and the layered reality model from Inception. The framework operates through multiple cognitive layers. L1 handles high-resolution semantic retrieval using vector embeddings and depth-scored memory. L2 builds associative intelligence through dynamic knowledge graphs and entity relationships. L3 introduces DREAMWEAVE’s core innovation: Pattern Geometry Matching, allowing the system to retrieve information based on structural similarity instead of keywords alone. The architecture also includes the Kick Mechanism, an epistemic consistency system that detects contradictions between surface retrieval and structural reasoning, triggering automatic re-retrieval and self-correction before generation occurs. DREAMWEAVE is fully LLM-agnostic and integrates with GPT, Claude, Llama, Qwen, and other OpenAI-compatible models. The entire system runs on AMD Instinct MI300X infrastructure using ROCm and vLLM with zero external API dependency, enabling complete data sovereignty and high-performance local inference. By combining vector search, knowledge graphs, structural pattern matching, asynchronous orchestration, and contradiction-aware retrieval, DREAMWEAVE represents a new architecture for intelligent memory systems and next-generation AI reasoning infrastructure.