Self-Aware Networks: A Systems Whitepaper on Distributed Neural Rendering, Memory Scaling, and Embodied Action

This paper presents a source-faithful modernization of Micah Blumberg's public Self-Aware Networks Whitepaper. It preserves the historical receive-transform-project architecture while developing a bounded and testable systems model of distributed neural rendering, memory reinstatement, receiver-relative temporal differences, thalamocortical concentration, embodied action, and Artificial Neurology. The manuscript separates established findings, documented historical SAN formulations, current SAN hypotheses, and engineering implications. Each major proposal is paired with comparators, measurement criteria, perturbation and rescue designs, limitations, and failure conditions. The source genealogy distinguishes owner-reported historical dates, public platform dates, immutable Git fixation, later restatement, and present synthesis. No new neural dataset or empirical confirmation is reported.