Marcelo Emanuel Paradela Teixeira , Independent Researcher, France

Large language models fuse knowledge and reasoning into billions of inscrutable parameters, trading interpretability for performance. We propose Semantic Topology Reasoning Architecture (STRA), which cleanly separates: (1) knowledge as explicit, inspectable semantic topology; (2) reasoning as meta-operations by smaller models (1-7B parameters) trained on topology navigation; (3) language as output interface, not cognitive substrate. This separation enables transparency (visible reasoning paths), efficiency (targeted computation), correctability (edit knowledge without retraining), and genuine cross-domain reasoning through semantic similarity. STRA integrates five primitives: Activation Arrays (working memory), Causal Signatures (cross-domain analogy), Selection Pressure (reasoning stability), Transform Learning (procedural compression), and Semantic Abacus (skill acquisition). These form a complete architecture for transparent, evolvable reasoning that operates on concepts, not tokens.

Semantic reasoning, transparent AI, knowledge representation, activation dynamics, explainable AI