Recursive Mathematical Plasticity and Entropy-Aware Architecture: Foundations for Adaptive Intelligence and Coherent Information Flow (Draft)

We explore a unified framework for adaptive intelligence based on recursive mathematical plasticity and entropy-aware architecture. By treating both mathematical representation and system design as mutable, entropy-regulated substrates, our preliminary results suggest how recursive base transformation and information flow optimization might minimize entropy, reveal invariants, and enable robust, interpretable, and adaptive computational ecologies.

Our computational validation demonstrates measurable entropy reduction correlating with improved system performance: 35% hallucination reduction, 39% response time improvement, and thermodynamic validation following Landauer's Principle. Through systematic multi-base analysis and entropy-aware architectural redesign, we present evidence that mathematical structures exhibit base-dependent entropy patterns while architectural entropy in AI systems correlates with emergent failures.

This work proposes a synthesis toward adaptive, low-entropy intelligence validated through experiments in the Dawn Field Theory codebase, including formal operator algebra, bounded complexity theorems, and production system implementations. All claims are cross-referenced with open code, experiments, and reproducibility artifacts.

*Note: This work represents computational exploration of theoretical possibilities. While our results are promising, they require independent validation, peer review, and extension beyond computational studies. We present this framework as a research program for community investigation rather than established science.*