Algorithmic Submersion Part II: Cross-Domain Architecture and Energy Autonomy

Any system pursuing computational efficiency under finite energy necessarily converges to a common architecture: binary substrate, hierarchical buffering, encapsulation, algorithmic submersion, and exception handling. This paper presents the Algorithmic Submersion Protocol as the unifying architectural grammar across biological neural networks and engineered computer systems, fully compliant with EET Core Rules v4.2. We demonstrate that the ``all-or-none'' firing of neurons and the binary switching of transistors are not evolutionary accidents but thermodynamic necessities derived from Postulate 3 (Discrete Transition). Binary search emerges as the optimal strategy for causal inference under energy constraints. Through encapsulation, frequently executed search paths solidify into cognitive blocks (in brains) or functions (in computers), a process directly tracked by the ontological derived parameter $d(t)$ (Submersion Depth). Hierarchical buffering serves as the energy-efficiency smoothing layer between explicit computation and implicit reflex. A key distinction between biological and artificial systems lies in energy autonomy: biological systems possess endogenous energy perception and regulation (η as an internal variable), while current computers remain energy-heteronomous. The emergence mechanism—blockage triggering inquiry—corresponds to exceptions and interrupts, while the ultimate limit of emergence is ontological reset, dictated by the Asymmetry Law of Topological Barriers (Ebᵐᵉˡᵗ Ebᶠᵒʳᵐ). This paper provides the phenomenological and architectural foundation for Algorithmic Submersion, complementing the rigorous thermodynamic formalism developed in Part I. : Algorithmic submersion; binary search; cognitive blocks; buffering; energy autonomy; von Neumann architecture; neural networks; cross-domain convergence