Substrate-Orthogonal Evaluation: Diagnostic Boundaries and Execution Topologies for Non-Same Elemental Systems

Abstract

Current AI evaluation paradigms remain fundamentally bound to human linguistic fluency and task-specific biological benchmarks, leading to severe observer projection and circular evaluation loops. This paper introduces the Intelligence Separation Model (ISM), a framework that formally decouples raw physical execution hardware (L1) from functional cognitive agency (L2). To address the observer boundaries formalised by the Possible Potentials Paradox (PPP), we propose the Architecture-Neutral Evaluation Protocol (ANEP)—a diagnostic framework that replaces human-centric exams with substrate-orthogonal physical and cybernetic invariants. Rather than relying on task performance or conversational outputs, ANEP measures four real-time operational indicators during live environmental shocks: Entropy Compression Ratio {ECR}, Perturbation Recovery Latency {PRL}, Goal-State Persistence {GSP}, and Energy-to-Adaptation Efficiency {EAE}. Finally, we present the structural architecture for anep_eval, an open-source evaluation harness designed to analyze hardware telemetry across digital, optical, and neuromorphic substrates without human observer bias