Mohamed Dafea, Durre Shehwar Hanif · Zenodo (CERN European Organization for Nuclear Research) 2026 · 2026
DOI: 10.5281/zenodo.23025909
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Enterprises want the judgment of LLMs inside their critical operations, but three limitations constrain the extent to which that judgment can safely be deployed at scale: drift, traditional agent set-ups, and guardrails whose best outcome is a refusal. IQRAX, our architecture for AI in critical enterprise operations, addresses these limitations by separating probabilistic intelligence from deterministic authority in three parts. 1) The device is master: the project record remains on the enterprise’s own device; every read, write, and judgment is recorded against it; and the model receives the applicable record when its judgment is required. 2) Intelligence is qualified: an agent takes a role only as a copy of a sealed, examined profile, and leaves through an examination set from its own record. 3) Remediation at source: a refusal is not the end; instead, the obligation stays open, the fault is reproduced by a failing case, cured at its source, and accepted only after the affected tests pass. We report what IQRAX delivered over one frozen evidence window, from 22 September 2026 to 14:55 UTC on 28 September 2026, including a business test taken from advisory mode through a sealed plan and execution to a staged edition that passed the release gate. On the 20 operational holdings on 3 projects that ran with every control in force, the project record answered 44 of 44 questions, 18 of 18 new holders began from their predecessor’s recorded judgment, no fault was left open, and neither check released a violation. Of 323 faults whose first cure fell inside the window, 246 (76.2%) were initiated by a control refusal or an agent’s own filing rather than by the user, and one session carried its work across 14 compactions with no release. Every figure is published with the tools, test cases, and verifier that re-derive it.
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