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This paper introduces ADMITBench, a rigorous framework designed specifically to evaluate the admissibility and safety of Large Language Model advisories within industrial settings. By enforcing explicit, non-compensatory checks against plant-specific profiles regarding evidence support, authority, and procedural compliance, it addresses critical gaps in deploying LLMs for high-stakes operational decisions. The release serves as a technical reference for researchers aiming to standardize how AI recommendations are validated before physical execution.
ADMITBench: A Safety-Governed Reference Framework for Evaluating the Admissibility of Industrial LLM Advisories
arXiv:2608.03866v1 Announce Type: new Abstract: This white paper presents ADMITBench, a reference framework for evaluating industrial LLM advisories at the level of the proposed action. The framework implements a versioned, safety-governed evaluation contract that checks whether a recommendation is supported by the available evidence, permitted under the stated authority and procedure, and acceptable under the plant-specific consequence checks encoded in the selected evaluation profile. In this report, \emph{safety-governed} means that eligibility is determined through explicit, non-compensatory checks derived from a versioned plant profile; it does not mean that the evaluator, model, or plant has been safety-certified. Release 0.1.0 is a public reference implementation for technical and research evaluation, not an authorisation for physical execution.
Abstract:This white paper presents ADMITBench, a reference framework for evaluating industrial LLM advisories at the level of the proposed action. The framework implements a versioned, safety-governed evaluation contract that checks whether a recommendation is supported by the available evidence, permitted under the stated authority and procedure, and acceptable under the plant-specific consequence checks encoded in the selected evaluation profile. In this report, \emph{safety-governed} means that eligibility is determined through explicit, non-compensatory checks derived from a versioned plant profile; it does not mean that the evaluator, model, or plant has been safety-certified. Release 0.1.0 is a public reference implementation for technical and research evaluation, not an authorisation for physical execution.
Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2608.03866 [cs.AI]
(or arXiv:2608.03866v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.03866 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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