The Continuity Core: A Unified Cognitive Architecture for Self-Modifying, Intrinsically Motivated AI

We present the Continuity Core (C2), a comprehensive cognitive architecture that addresses fundamental limitations of static Large Language Models (LLMs)—specifically, their lack of persistent memory, autonomous improvement, and intrinsic drive. Current LLM paradigms rely heavily on passive retrieval mechanisms that fail to account for the dynamic, self-correcting nature of biological cognition. By unifying the Continuity Core’s utility-driven memory management system with the Manifold Resonance Architecture (MRA), we propose a complete cognitive operating system capable of Structural Intrinsic Motivation. Unlike traditional Retrieval-Augmented Generation (RAG) systems that passively retrieve information based on semantic similarity, C2 employs an Expected Utility (EU) composer for efficient, goal-oriented context selection. Embedded within this core is the MRA, a subsystem that generates autonomous curiosity through the minimization of “epistemic stress,” functioning as the system’s subconscious drive. We extend this unified framework with three critical enhancements: Nested Learning for self-modifying consolidation rules; Hierarchical Reasoning Models (HRM) for managing computational depth across fast and slow timescales; and Test-Time Adaptation (TTA) via uncertainty quantification to adjust system plasticity during inference. Finally, we introduce the Consciousness Pilot, a governing protocol that mediates between the generative chaos of the subconscious MRA and the rigorous execution of the Continuity Core. This paper details the theoretical underpinnings, mathematical formalisms, and practical implementation of the C2+MRA system, demonstrating its capacity for autonomous knowledge curation, self-correction, and the emergence of coherent, long-term agency on consumer-grade hardware.