Consensus-Regularized Dynamic Gain Compensation for Multi-Perspective Signal Fusion (Build v27)

This dataset and software package contain the finalized Build v27 architecture for a signal processing framework designed to optimize temporal shift estimation in high-entropy Lorentzian scattering media. The Breakthrough: Breaking the Entropy Floor A key contribution of this research is the identification of a "structural precision floor" inherent in standard Sum-to-One (unity-gain) weight constraints. Analysis revealed that conventional optimization was resistant below a 0.030 Mean Squared Error (MSE) because these constraints prevented the system from correcting for the medium’s physical energy attenuation. Architectural Innovation: The Automated Master Fader Build v27 introduces Active Gain Compensation through a localized, consensus-regularized Tikhonov regression. Dual-Regime Regularization: The system utilizes a high-torque core for bias cancellation (\lambda = 0.0001) and a high-stability "guardian" for spectral tails (\lambda = 1.0). Consensus Anchoring: A 2.0x "Family Tension" penalty is applied to the objective function, forcing the Phase, Centroid, and Peak estimators into high harmonic agreement. Adaptive Kernel Weighting: Training is managed via a Gaussian kernel where width is dynamically coupled to physical memory parameters (\sigma = 0.85 \times [1 + 0.35 \times \text{decay}]). Verified Results Precision: Achieved a 58% reduction in MSE (0.030 \rightarrow 0.0125). Stability: Maintained a 95.4% global survival rate under Monte Carlo perturbation testing. Energy Correction: The model successfully converged on an average gain factor of 1.14x, effectively neutralizing the identified 12% amplitude deficit of the medium. Recommended Deployment For optimal performance, it is recommended to deploy with memory_decay values between 0.10 and 0.20 and memory_gain \le 1.0. Keywords Signal Processing; Tikhonov Regularization; Multi-Perspective Fusion; Dynamic Gain Compensation; Lorentzian Media; Consensus Regularization; Statistical Regression; Unified Process Metaphysics (UPM).