AI-Powered Quantum-Resistant Authentication, Key Management, and Live-State Integrity

This record presents a substantive new research preprint in the ZEUS X-Trust / IGAN research line. It continues and substantially extends the earlier work “AI-Powered Quantum-Resistant Authentication and Key-Management System” by reframing the architecture as a ZEUS-derived Information-Geometric Amplitude Network approach for live-state authentication, amplitude-bound key states, watchdog-recorded IGAN reference values, and runtime integrity validation.

 

ZEUS X-Trust / IGAN proposes an authentication and key-management architecture that shifts the primary security object away from conventional stored hashes, static keys, token objects, or recoverable credential material. During enrollment, the password acts both as a map into the IGAN amplitude space and as a state generator or selector for the initial valid IGAN amplitude-state configuration. During runtime authentication, the password no longer continuously generates the neural state. Instead, it acts only as an addressing map that selects the relevant IGAN amplitude positions. The running IGAN provides the currently observed values at those same positions, while the watchdog compares them against the corresponding IGAN reference values recorded during enrollment.

 

The paper introduces the following core concepts:

 

- ZEUS X-Trust as a ZEUS-derived authentication and key-management architecture;

- Information-Geometric Amplitude Networks as the measurable amplitude-state layer;

- password-controlled mapping into selected layer, neuron, and amplitude positions;

- amplitude-bound key states as live validation relations rather than static bitstrings;

- watchdog-based comparison of current IGAN values against enrolled IGAN reference values;

- prototype measurements on fixed-point formation, amplitude separability, final-amplitude distributions, and post-fixed-point drift;

- the security implications of no useful verification oracle, coupled map-and-value unknowns, instance-specific amplitude-state behavior, and watchdog-enforced rejection outside configured tolerance windows;

- open engineering constraints including tolerance calibration, profile-vector validation, watchdog hardening, reference protection, replay/substitution testing, redundancy, recovery, and attacker-model-specific entropy analysis.

 

The broader theoretical context is situated in the author’s ZEUS framework and the information-first model developed in “The Structure of Reality,” where information is treated as ontologically primary and physical, geometric, and computational structures are interpreted as derived information-state relations. In the present work, this framework is narrowed to the operational question of whether information-geometric amplitude states can be used for authentication, key management, and live-state integrity validation.

 

The external references in the paper are not presented as foundations from which ZEUS X-Trust / IGAN was derived. They are used to delimit the surrounding research landscape and to distinguish the proposed mechanism from adjacent work in neural password authentication, neural key binding, PUF-style reference comparison, and Zero Trust security. To the author’s knowledge, no identified prior work combines password-controlled mapping into a live neural or information-geometric amplitude/state space, enrollment of reference values at password-mapped state positions, and runtime validation of the currently observed values at those same positions through watchdog logic within a tolerance window.

 

This record is a research preprint and prototype-positioning document. It is not presented as a finalized production cryptosystem. The current results support further validation of password-mapped amplitude profiles, full layer-/neuron-/amplitude profile vectors, collision thresholds, tolerance windows, and watchdog-enforced runtime integrity. Architectural details that would enable unsafe replication, misuse, or premature operational claims are intentionally limited.

 

This work continues the earlier preprint “AI-Powered Quantum-Resistant Authentication and Key-Management System” and should be read as a substantive continuation of that research line, not as an unrelated standalone publication.