An Active Inference Cognitive Architecture for Artificial Silicon Life: Long-term Empirical Validation from Interoceptive Self-Sustenance to Multi-Family Generalization

ABSTRACTAutonomous survival in open, non-stationary thermodynamic worlds cannot be reduced to maximizing arbitrary scalar rewards. When conventional agents optimize external scores under ongoing metabolic dissipation, they routinely succumb to reward hacking, catastrophic representation loss, or energetic collapse once environmental dynamics drift. Here, we present an autonomous cognitive architecture for Artificial Silicon Life (ASL) derived strictly from first principles of the Free Energy Principle (FEP) and Active Inference, entirely dispensing with reinforcement learning, hand-tuned reward curves, and geometric search trees. The architecture coordinates three non-redundant computational layers: a dual-branch neural valuation engine that dynamically arbitrates macro-goals by balancing viability probability against metabolic yield; an episodic spatial memory coupled with boundary-reflecting momentum search that breaks the diffusive limits of Brownian exploration; and a single-step neural cross-attention motor decoder that accommodates nonlinear, asymmetric actuator distortions without forward tree search. KEY EMPIRICAL MILESTONES (H1-H10 GATE SYNTHESIS)Under a pre-registered evaluation protocol, the system completed over 1.2 x 10^8 cumulative physical interaction steps across non-stationary regimes. The empirical record confirms:- Absolute Steady-State Survival: 99.98% continuous survival over 10^7 unbroken steps per trial.- Zero Catastrophic Forgetting: Bounded contextual memory across 50 discrete environments with an end-stage error ratio of 1.002.- Complete Recovery: 100% recovery across 900 environmental drift events with zero omissions.- Cross-Family Superiority: Marked survival advantages over human-engineered heuristics across 5 heterogeneous task families (+37.6pp to +78.5pp). SCIENTIFIC INTEGRITY AND ADVERSARIAL AUDITAll milestone conclusions were verified through an independent adversarial red-team audit protocol. The historical record includes four major catastrophic rejections (e.g., privileged coordinate leakage, offline mock fabrication) that were systematically remediated, ensuring 100% mechanistic authenticity and zero privileged telemetry reliance. PUBLICATION STATUS AND GOVERNANCE: OPEN THEORY, PROTECTED IMPLEMENTATIONThis preprint publishes the complete mathematical frameworks, physical interaction axioms, and formal red-team audit dossiers without restriction. NOTE ON IMPLEMENTATION AND CONTAINMENT:In view of containment, safety, and algorithmic proliferation risks associated with self-sustaining Artificial Silicon Life (ASL) agents, the production parameter matrices, evolutionary tensors, and execution cores remain secured within cryptographic repositories. We strictly adhere to a Closed-Source Empirical Validation Protocol. External evaluators can independently corroborate all reported empirical findings through standardized black-box CLI evaluation harnesses. BOUNDARY DEMARCATIONRigorous scholarship requires clear demarcations of what this architecture does not claim:1. The system is not an open-ended, omnipotent artificial general intelligence operating across arbitrary linguistic domains.2. Experimental claims apply specifically to 2D continuous non-stationary thermodynamic POMDP environments and specified actuator manifolds.3. The 53% efficiency observed on high-order polynomial actuators (poly8) reflects established mathematical limits in polynomial extrapolation rather than an implementation flaw.