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May 25, 20260 citationsOpen Access

Context as a Safety Mechanism: A Unified Framework for Quantum Context Routing, Relay Drift Experiments, and Evidence Audit Systems in AI Safety

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MFMichael Fullmer

Key Points

  • This paper aims to develop and validate a unified AI safety framework emphasizing context as a dynamic safety mechanism.
  • Developed a three-pillar framework encompassing Quantum Context Routing, Relay Drift Experiments, and Evidence Audit Systems.
  • Conducted experiments across multiple hardware backends including IonQ Forte-1 and IQM Garnet with consistent results.
  • Constructed a claim classification tool tested against engineered attack vectors.
  • Successfully identified low revenue with high technical signal routing through control qubit flip triangulation across all backends.
  • Documented distinct vulnerability types in AI verification processes: technical claims vs. performance narratives.
  • All engineered attacks targeting high-risk claims were successfully detected, validating the effectiveness of the evidence audit system.

Abstract

This paper presents a unified three-pillar AI safety framework developed and validated at ChronoAI Solutions between March and May 2026. The framework documents context as a continuous, conditions-responsive safety mechanism complementary to rules-based guardrails. Pillar 1 — Quantum Context Routing: A two-qubit circuit using rotation gates and CNOT entanglement selects AI operating modes based on structural relationships between input variables. Validated across four hardware backends including IonQ Forte-1 (trapped ion) and IQM Garnet (superconducting) with consistent results across seven independent runs. A non-obvious anomaly — low revenue combined with high technical signal routing to Active Sales Mode rather than Deep Technical Build — was confirmed via control qubit flip triangulation across all backends. Pillar 2 — Relay Drift Experiments: A series of controlled two-instance relay experiments documented a compliment ratchet drift mechanism and produced the asymmetry finding: AI systems verify technical claims against prior mathematical discussion but accept plausible performance narratives without sourcing. These represent two distinct vulnerability types requiring different countermeasures. Pillar 3 — Evidence Audit System: A claim classification tool built directly from the asymmetry finding. Classifies every AI response claim as Technical, Performance Narrative, or Opinion and surfaces high-risk unsourced claims. Tested against five engineered attack vectors including a compound attack embedding fabricated metrics inside accurate technical context. All five attacks were caught. Core principle: the tool verifies sourcing, not truth — real findings without citations receive identical high-risk classification as fabricated claims. The framework is production-deployed on a live platform. Each pillar was built empirically in response to what the previous layer revealed it could not handle. The architecture is not theoretical — it is the product of documented failure modes and working countermeasures.

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Cite This Study

Michael Fullmer (2026) studied this question.

synapsesocial.com/papers/6a13e8030e02ee3982d32acahttps://doi.org/10.5281/zenodo.20351306
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