Deterministic Semantic Reconstruction (DSR): A Cognitive Framework for Meaning Recovery from High-Entropy Data

Deterministic Semantic Reconstruction (DSR) is a cognitive AI framework designed to recover coherent meaning from high-entropy, fragmented, or multilingual data using deterministic, instruction-driven processing instead of probabilistic generation. The framework operates through a fixed four-stage pipeline—Parsing, Translation, Semantic Synthesis, and Expression—ensuring full reproducibility, transparency, and auditability. This work presents the theoretical foundations of DSR together with its implementation in the KIOSK-2 system. Evaluated on 81 heterogeneous product records (input entropy 82/100), the deterministic pipeline achieved 86% reconstruction completion, 62% entropy reduction, 87% translation accuracy, and a 76% usable output rate. All computations were entirely deterministic, with no random sampling or probabilistic components. The results demonstrate that meaning can be algorithmically reconstructed from noisy data and establish DSR as a new paradigm of reconstructive intelligence—an AI architecture focused on semantic restoration, not prediction. This approach supports explainability, stability, and regulatory compliance in high-integrity AI applications.