Reality-Anchored Cognitive Memory and Thinking Architecture (RACMTA): Toward Purposeful, Survival-Aligned Intelligence Beyond Statistical Reasoning

Current artificial intelligence systems achieve impressive performance through statistical pattern recognition, yet they remain limited by the absence of persistent, reality-anchored memory, explicit distinction between fact and internally generated possibility, long-horizon reasoning, structured uncertainty handling, and purpose-aligned evaluation. This paper introduces the Reality-Anchored Cognitive Memory and Thinking Architecture (RACMTA), a unified framework in which memory, evaluation, simulation, gap detection, and decision-making operate as integrated components of a coherent cognitive system. The architecture includes:- Survival-weighted memory, where all stored data carries evaluative significance- Typed memory states, distinguishing observed, inferred, simulated, and imagined information- Vector-based thought environments enabling structured reasoning across competing hypotheses- A Missing Data Detection Engine (MDDE) that identifies unknowns and prevents premature conclusions- Persistent reasoning across time- A dual decision layer separating ideal truth-seeking from real-world constraint-based action To support practical implementation, the paper provides a minimal computational model, data structures, algorithms, diagrams, worked examples, and a roadmap for incremental development. The central claim is that intelligence does not emerge from scale alone, but from architecture. Intelligence is defined as the ability to produce outcomes that support survival and stability across time under uncertainty. RACMTA is proposed as a foundational step toward general and ethical artificial intelligence systems capable of structured reasoning, uncertainty awareness, and long-term alignment with human and civilizational well-being.