This repository contains the complete empirical validation of Bhosale’s Inverse Scaling Law, demonstrating that in deterministic, modular AI systems with explicit honest confidence signals and early termination authority, average inference cost decreases as system capability increases. The work is implemented as a reference LEGO-MoE MVP architecture, featuring: Deterministic expert routing Justification-based confidence (zero false high-confidence errors) Integrity-first gatekeeping Safe early termination Sub-millisecond cache hits The archive includes a fully reproducible automated validation suite, raw empirical proof artifacts, latency and confidence visualizations, and complete documentation. Results show a 60.8× average latency reduction versus baseline, with 100% determinism, 87.2% cache hit rate, and zero false high-confidence errors, empirically validating inverse scaling behavior. This work establishes an architectural regime distinct from classical monolithic scaling, with implications for edge deployment, cost-efficient intelligence, and uncertainty-proportional computation.