Compression–Decompression Control for AI Training

This work presents a control-theoretic framework for artificial intelligence training based on alternating compression and decompression regimes. Rather than treating learning as monotonic loss minimization, training is modeled as traversal through distinct operating regimes that must be actively regulated to maintain stability, generalization, and memory retention.High-compression phases correspond to constrained, task-driven learning (e.g., supervised optimization and regularization), while decompression phases enable exploratory expansion via noise injection, self-supervision, or data augmentation. A mandatory return (recompression) phase consolidates representations and prevents instability, hallucination, and catastrophic forgetting.The paper defines measurable stability proxies—such as gradient variance, representation entropy, curvature metrics, and logit volatility—and specifies decision rules for switching between regimes. The resulting controller can be implemented as a supervisory training scheduler without modifying model architecture.This framework clarifies the relationship between existing training heuristics (e.g., curriculum learning, cyclical learning rates, replay, and consolidation methods) by unifying them under a single regime-control principle. Expected benefits include improved generalization, reduced hallucination, and increased training robustness across modern deep learning systems, including transformer architectures.This work is derived as an application-layer module of the Unityverse Kernel Spec v1.0 (figshare DOI:10.6084/m9.figshare.31173967). No kernel assumptions are modified.