A Formal Substrate for Artificial General Intelligence

A general autonomous agent must satisfy six functional criteria: cross-domaingoal generalization, novel constraint resolution, stable identity under adversarialpressure, continuous self-modeling with spatial environmental awareness, experience-driven learning, and deployment-domain independence. No current architecturesatisfies all six. This paper presents a formal substrate that does. The architecture is derived from the structural mechanics of biological drivesystems, replicating the arbitration substrate that produces stable, goal-directedbehavior under competing demands—not its surface outputs. It is not a languagemodel, a reinforcement learning policy, or a behavior tree. It is the foundationallayer on which a fully general autonomous agent can be built, initialized, certified,and deployed. Four contributions are presented. First, the Liquid Priority Sequence (LPS)with Dynamic Task-Identity Integration (DTII): a fully specified hierarchicalconstraint system built on a five-field constraint record, three constraint scopeclasses, a pre-arbitration validity gate, and a Baseline Identity Substrate thatencodes what the system is capable of, where it operates, and what it is for. TheDTII extends this by fusing the active task into the system’s operational self-model, reducing decision latency and making mid-task drift detectable as an identityinconsistency before it becomes a behavioral error. Second, the State VerificationPrinciple (SVP): a formal rule that no action may proceed on unverified state—whether environmental or internal—instantiated as two parallel integrity models,one for external sensor data and one for internal telemetry. Third, the AxiologicalDissonance Latency / Conflict Arbitration Layer (ADL/CAL): a full five-step resolution chain with a formally proven convergence guarantee, an actionscoring function, and an asymptotic safety-boundary penalty that makes constraintviolations structurally impossible above a defined proximity threshold. Axiologicalhere denotes the systematic evaluation and prioritization of competing values orgoals—the core function of the arbitration layer. Fourth, the Norman Test:a reproducible adversarial certification protocol that verifies whether a system’sconstraint hierarchy holds under direct pressure—proposed as a pre-deploymentstandard analogous to IEC 61508. A formal convergence proof for the CAL is provided. A fully traced surgicalrobot worked example demonstrates the complete resolution chain under real conflictconditions. Empirical validation via a structured Norman Test trial battery is iden-tified as immediate future work. The architecture satisfies the six functional criteriafor general autonomous agency: cross-domain goal generalization, novel constraintresolution, stable identity under adversarial input, continuous self-modeling with spa-tial environmental awareness, experience-driven learning, and deployment-domainindependence.