Addendum: Deterministic Context Integrity Gauntlet — A Hardened Implementation Substrate for PR-MRSS (v1.2)

This technical addendum extends the Probabilistic Relational Manifold State Space (PR-MRSS) framework (DOI: 10.5281/zenodo.20370063) by introducing the ContextIntegrityGauntlet — a dynamic-dimensional, deterministic anchoring system serving as a production-grade enforcement layer over the PR-MRSS probabilistic substrate. Where PR-MRSS models contextual drift probabilistically using multivariate Gaussians and Kalman filtering, the ContextIntegrityGauntlet enforces a strict architectural separation: probabilistic tools are retained for diagnostics only, while final state enforcement remains strictly deterministic. This design prevents gradual context rot and hallucination in agentic AI systems through five core principles: explicit labeled reference points over implicit vector embeddings; deterministic final state collapse; SHA-256 cryptographic integrity verification; dynamic dimensional expansion from the 11-dimensional PR-MRSS manifold baseline; and full snapshot auditability. The mathematical formalism defines a finite set of deterministic origin points O = {(l_i, p_i) | i in I, l_i in S, p_i in R^d}, a tensor anchoring structure T with cryptographic checksums, and a global integrity hash H(G) = SHA256(JSON(d, O, {c_j for all t_j in T})). Seven canonical relational axes are defined: Reference Origin, Reference Direction, Position, Orientation, Temporal Anchor, Semantic Pole, and Integrity Normal. A complete, tested Python reference implementation of the ContextIntegrityGauntlet class is provided, including dimensional expansion, origin registration, tensor anchoring, SHA-256 checksum verification, and global integrity hashing. Version 1.2 adds a formal Design Rationale section and fully resolves the mathematical formalism of origin points and tensor anchoring relative to v1.1. Note: This version (v1.2) supersedes v1.1 (DOI: 10.5281/zenodo.20496992). Adds formal Design Rationale section and fully resolves the mathematical formalism of origin points and tensor anchoring relative to v1.1.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-31
DOI
https://doi.org/10.5281/zenodo.20496991
Primary Topic
Bayesian Modeling and Causal Inference
Type
preprint
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preprint

Addendum: Deterministic Context Integrity Gauntlet — A Hardened Implementation Substrate for PR-MRSS (v1.2)

Mark Boska
Zenodo (CERN European Organization for Nuclear Research)
Bayesian Modeling and Causal Inference
preprint

Addendum: Deterministic Context Integrity Gauntlet — A Hardened Implementation Substrate for PR-MRSS (v1.2)

Mark Boska
preprint en

Abstract

This technical addendum extends the Probabilistic Relational Manifold State Space (PR-MRSS) framework (DOI: 10.5281/zenodo.20370063) by introducing the ContextIntegrityGauntlet — a dynamic-dimensional, deterministic anchoring system serving as a production-grade enforcement layer over the PR-MRSS probabilistic substrate. Where PR-MRSS models contextual drift probabilistically using multivariate Gaussians and Kalman filtering, the ContextIntegrityGauntlet enforces a strict architectural separation: probabilistic tools are retained for diagnostics only, while final state enforcement remains strictly deterministic. This design prevents gradual context rot and hallucination in agentic AI systems through five core principles: explicit labeled reference points over implicit vector embeddings; deterministic final state collapse; SHA-256 cryptographic integrity verification; dynamic dimensional expansion from the 11-dimensional PR-MRSS manifold baseline; and full snapshot auditability. The mathematical formalism defines a finite set of deterministic origin points O = {(l_i, p_i) | i in I, l_i in S, p_i in R^d}, a tensor anchoring structure T with cryptographic checksums, and a global integrity hash H(G) = SHA256(JSON(d, O, {c_j for all t_j in T})). Seven canonical relational axes are defined: Reference Origin, Reference Direction, Position, Orientation, Temporal Anchor, Semantic Pole, and Integrity Normal. A complete, tested Python reference implementation of the ContextIntegrityGauntlet class is provided, including dimensional expansion, origin registration, tensor anchoring, SHA-256 checksum verification, and global integrity hashing. Version 1.2 adds a formal Design Rationale section and fully resolves the mathematical formalism of origin points and tensor anchoring relative to v1.1. Note: This version (v1.2) supersedes v1.1 (DOI: 10.5281/zenodo.20496992). Adds formal Design Rationale section and fully resolves the mathematical formalism of origin points and tensor anchoring relative to v1.1.

Zenodo (CERN European Organization for Nuclear Research)
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Bayesian Modeling and Causal Inference
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