Persistent Cognitive Architectures for Human-AI Co-Reasoning: Beyond Retrieval to Structural Intelligence

Large language models excel at semantic reasoning but remain fundamentally stateless, reactive, and unable to sustain long‑term collaborative cognition. Retrieval‑augmented generation (RAG) extends access to external knowledge but does not provide structural reasoning, temporal continuity, or proactive initiative. We present Symbiose, a persistent cognitive architecture that integrates graph‑based causal reasoning, vector‑based semantic retrieval, hierarchical memory consolidation, and proactive autonomy into a unified system for human–AI co‑reasoning. Central to the architecture is Quantum Leap (QL), an orchestration layer that coordinates graph–vector fusion, causal extraction, preference learning, and multi‑level temporal consolidation. Symbiose introduces an L0–L3 epistemic stratification framework that distinguishes canonical facts, derived inferences, evidence‑supported claims, and LLM‑generated hypotheses, enabling explicit provenance and epistemic transparency. Through a year‑long deployment in the development of Energy‑Flow Cosmology, we demonstrate emergent capabilities not achievable in stateless LLM systems, including cross‑session identity linking, evidence‑gap detection, multi‑hop causal reasoning, and context‑aware proactive suggestions. We discuss architectural implications, limitations, and future directions for persistent human–AI collaboration.