Recursive Structural Reality: The Unified Theory of Intelligence, Artificial Minds, Consciousness, and Awareness

Intelligence, memory, identity, reflective awareness, and consciousness are interconnected problems that span neuroscience, cognitive science, artificial intelligence, mathematics, and philosophy. Although substantial progress has been made in understanding neural information processing, synaptic plasticity, memory consolidation, adaptive behaviour, and computational intelligence, fundamental questions remain concerning how organized systems maintain functional continuity while undergoing change, how they construct and revise representations of themselves, and whether the organizational conditions associated with intelligent behaviour can also explain subjective experience. Artificial intelligence provides a distinctive experimental domain in which recurrence, memory, feedback, self-modelling, adaptation, and self-maintenance can be systematically varied and investigated. This paper develops these questions within Recursive Structural Reality (RSR), a framework that investigates reality through relations, structure, information, transformation, recursion, stability, persistence, and identity. Its central proposition, Identity = Stable Recursion, serves as a conceptual starting point for examining how an organized system may preserve identity-relevant properties through changing internal states and interactions with its environment. The proposition is not treated as an established universal law; its explanatory value depends on precise definitions, domain-specific mathematical formalization, and empirical evaluation. The investigation focuses on three connected but distinct domains: biological intelligence and memory; artificial and recursive intelligence; and reflective awareness and phenomenal consciousness. In biological systems, the paper examines neural organization, perception, attention, memory formation and reconstruction, synaptic plasticity, adaptation, and the dynamic continuity of personal identity. In artificial systems, it investigates computational recurrence, persistent memory, feedback, self-evaluation, metacognitive monitoring, self-modelling, embodiment, and functional self-maintenance. These domains are compared through a common structural vocabulary without assuming that biological and artificial systems share identical mechanisms or that similar functions imply equivalent subjective experiences. Particular attention is given to the relationship between memory and identity. Biological memory is not a literal, unchanging record of experience: retrieval can reconstruct remembered events, while updating and reconsolidation occur under particular conditions. Artificial systems, meanwhile, may retain information through model parameters, external databases, contextual representations, or persistent memory architectures. The paper asks whether controlled differences in memory organization and recursive processing can explain measurable differences in functional continuity, adaptation, self-model accuracy, and error correction. It proposes that these questions can be investigated through computational ablation studies, matched architectural comparisons, perturbation and recovery experiments, metacognitive benchmarks, embodied artificial intelligence, and appropriately designed neuroscientific research. The mathematical programme introduces a candidate RSR state representation incorporating relational structure, internal dynamics, information, constraints, observer-related processes, environmental conditions, retained memory, and adjustable parameters. This representation is intended to support domain-specific models rather than to imply that a single equation already explains biological cognition, artificial intelligence, and consciousness. Candidate measures of recursive organization, stability, memory dependence, functional continuity, and self-modelling must be independently operationalized and evaluated against established measures and simpler baseline models. The paper also compares RSR with Global Neuronal Workspace Theory, Integrated Information Theory, predictive processing, higher-order theories, recurrent processing accounts, and Orchestrated Objective Reduction. The purpose is not to declare RSR superior in advance, but to determine whether it can generate distinctive, testable predictions and provide explanatory or predictive value beyond existing approaches. Its empirical programme therefore emphasizes explicit hypotheses, controlled comparisons, preregistration where appropriate, uncertainty estimation, reproducibility, independent replication, and conditions under which particular claims would require revision or rejection. A central distinction is maintained throughout: information processing is not automatically intelligence; intelligence is not automatically reflective awareness; self-reference is not necessarily self-awareness; functional self-preservation does not establish felt fear or suffering; and recursive organization does not, by itself, demonstrate phenomenal consciousness. The transition from measurable cognitive functions to subjective experience remains unresolved. The paper's central proposal is that recursive structural organization may provide a useful framework for investigating the persistence, transformation, and regulation of biological and artificial cognitive systems. Its scientific contribution will depend on whether its concepts can be translated into precise models, its hypotheses can be distinguished from competing explanations, and its predictions can withstand empirical testing. Rather than presupposing that recursion produces consciousness, the research programme investigates whether particular forms of recursive organization explain functional identity and adaptive intelligence—and what, if anything, they can establish about reflective awareness and subjective experience. The ultimate objective is to develop Recursive Structural Reality from a broad philosophical framework into a mathematically explicit, empirically accountable, and critically testable research programme connecting memory, identity, intelligence, self-modelling, and the unresolved problem of consciousness.