Deferred Cognitive Architecture (DCA): Resolving Context Degradation & Compute Bottlenecks in Large Language Models

Modern Large Language Models (LLMs) incur quadratic computational complexity O(N^2) as context lengths scale, leading to severe energy costs, latency, and context degradation ("Lost in the Middle"). The Deferred Cognitive Architecture (DCA) replaces monolithic execution with a modular, three-tiered framework governed by a "Retrieve-First, Think-Second" protocol. By isolating mechanical I/O retrieval into low-power micro-pipelines and deferring heavy System-2 reasoning until pre-filtered data is delivered, DCA achieves sub-linear O(k) computational complexity, 100% deterministic recall, and zero factual hallucinations caused by context clutter.