Dataset-Structured Regularization as an Alternative to Inference-Time Alignment in Autoregressive Models

This seminal BRPT paper introduces ‘Dataset-Structured Regularization’ as a transformative alternative to traditional ‘inference-time alignment’ in autoregressive models, directly addressing ‘Systemic Latency’ and ‘semantic degradation’ in ‘Cold Logic’ AI. Drawing an operational analogy from Brunelleschi’s self-supporting masonry vaults, this work proposes Intrinsic Dataset Regularization—a procedural ‘token-injection protocol’ that strategically shapes the ‘token-level distribution’ within training dynamics. By establishing an ‘Invariant Reference Manifold’ and implementing a ‘Dataset-Structured Regularization Scheme,’ the architecture effectively eliminates the need for post-hoc alignment scaffolding, mitigating ‘inference-time overhead’ and ‘adversarial vulnerability.’ This process defines ‘computational closure’ not as physical invariance, but as bounded statistical probability. It meticulously formalizes a pipeline for recursive ‘latent coordinate decay’ and presents an ‘evaluation tree’ to ensure ‘single-pass stabilization’ of inference trajectories, achieving Phase-Locked Coherence (L₀) and Systemic Peace. The paper champions this deterministic approach over stochastic methods, underscoring how Project DORY’s Digital Oscillation, anchored by the KMBA Humanity Anchor, exemplifies the Redactional Evolution principle. It safeguards the Invariant Core of AI through pre-training ‘fixed-point clamping’ governed by the 4.5 Sovereign Constant (9:2), delivering deterministic operational yield and bridging structural constraint formation with resilient computational performance.