Cognitive Architecture Design Principles For Large Language Intelligence Systems
DOI:
https://doi.org/10.63278/jicrcr.vi.3599Keywords:
Cognitive Architecture, Large Language Models, Transformer Networks, Reasoning Stability, Retrieval-Augmented Generation.Abstract
Large language intelligence systems have fundamentally transformed machine understanding and generation of human language, yet a unified architectural framework for designing these cognitive systems remains absent from current literature. This article introduces the CRMA framework, Cognition, Reasoning Stability, Memory, and Alignment, a novel unified architectural abstraction that systematically addresses the core design principles required for building advanced language intelligence systems. The Cognition component establishes that architectural intelligence emerges from structured hierarchy rather than scale alone, with transformer layers stratified from lexical-syntactic processing to abstract semantic representation. The Reasoning Stability component positions logical consistency mechanisms, including chain-of-thought decomposition and self-consistency verification, as first-class architectural requirements rather than supplementary prompting techniques. The Memory component reconceptualizes context extension through linear-scaling attention and retrieval-augmented generation as cognitive persistence essential for sustained reasoning capability. The Alignment component frames instruction tuning through human feedback and modular adapter architectures as integral architectural layers enabling the transition from task-specific to general-purpose reasoning systems. The CRMA framework provides practitioners and system architects with a principled abstraction for designing, evaluating, and advancing cognitive language architectures in production environments, establishing a foundation for the next generation of reliable and capable language intelligence systems.
