Isomorphic Cognitive-Execution Architecture: Trans-Substrate Invariants for Bare-Metal Safety and Human-Machine Alignment

Doc Reference: ARC-PUB-2026-META01 Primary Suite DOI Anchor: 10.5281/zenodo.22665852 Master Target Hash: A-77-DELTA-SHIELD-LOCKED Abstract: Current artificial intelligence safety paradigms rely predominantly on high-level, probabilistic guardrails—such as Reinforcement Learning from Human Feedback (RLHF), supervisor models, and natural language system prompts—operating within dynamic execution software layers. These methods introduce significant operational compute costs (C_ops > 0), high latency, and vulnerability to intent-parser bypasses (jailbreaks). This paper formalizes Isomorphic Cognitive-Execution Architecture, a deterministic framework within Admissibility Science that bridges human cognitive boundary philosophy and bare-metal hardware execution through 1:1 mathematical structural invariance. Implemented in ![no_std] Rust without dynamic memory allocation, the system enforces static SRAM bounds (S_max <= 4096B), hardware timer clamps (tau_override <= 11.99ms), and O(1) Poset join-semilattice evaluation under Axiom I_3 (Zero Ambient Authority). We demonstrate that the invariant rules governing human mental equilibrium under high-entropy conditions mirror the physical mechanics required to prevent non-deterministic state mutation (Delta_external = 0) on raw silicon, providing a zero-overhead, trans-substrate foundation for autonomous agent safety.