Consolidated Technical Report, Mathematical Formalization, and Research Agenda This technical report consolidates a body of completed experimental work on stability-first learning in artificial neural networks. The central thesis is that standard optimization based on fixed steps and global updates is insufficient for robust sequential learning. Instead, the report documents mechanisms that prioritize representation stability, modularity, and endogenous time perception. The report details the implementation and operational outcomes of four key studies: Active Sleep: A generative replay mechanism for memory consolidation without storing original data. Temporal LoRA: A modular architecture using context-dependent routing to minimize interference between tasks. The Lazarus Effect: Empirical demonstration of capability recovery after structural damage (pruning/noise) without access to original training data. Recursive Time Depth: A training criterion based on internal stabilization metrics rather than fixed optimization steps. The Physics of Learning Time In the final section, the report outlines an interpretive theoretical framework derived from these experiments. It proposes reinterpreting learning not as gradient descent steps, but as a trajectory through a phase space defined by plasticity, stability, and entropy. Key concepts introduced include endogenous chronometry (internal time), hysteretic barriers to forgetting, and identity defined as phase stability. Code Availability All implementations, logs, and reproduction scripts associated with this report are available at the accompanying repository: https://github.com/vitali-sialedchyk/stability-first-ai
Vitali Sialedchyk (Sun,) studied this question.