Structured Intelligence: An Architectural Blueprint for Causal, Interpretable, and Robust AI Systems

This article investigates the fundamental limitations of modern artificial intelligence systems, including the black box problem, generation of unreliable information (hallucinations), ethical risks, and limited robustness. It proposes an architectural concept combining vector representation decomposition with explicit interpretable axes, Compressed Semantic Tokens (CST) for abstract concept representation, and Cause-Condition-Consequence (CCC) Blocks for modeling formal logic. Key innovations include mechanisms for context-dependent refusal, separate confidence metrics, and a modular system with decentralized knowledge repositories. It is assumed that this approach is highly effective for structured areas, while recognizing its fundamental limitations in subjective areas.