{"created": "2026-08-15T07:47:28.825649+00:00", "modified": "2026-08-15T07:47:29.065962+00:00", "id": 21943877, "conceptrecid": "21943876", "doi": "10.5281/zenodo.21943877", "conceptdoi": "10.5281/zenodo.21943876", "doi_url": "https://doi.org/10.5281/zenodo.21943877", "metadata": {"title": "Machine Entity Comprehension: A Black-Box Conformance Framework for Identity, Context and Change", "doi": "10.5281/zenodo.21943877", "publication_date": "2026-08-15", "description": "

Machine Entity Comprehension defines a black-box conformance framework for evaluating whether a system can preserve entity identity, distinguish contextually similar entities, maintain relevant relationships and constraints, respond coherently to material changes, contain contradiction and produce repeatable outcomes under equivalent declared state.

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The paper establishes the conformance layer for Geometry Intelligence Infrastructure, a proposed class of evidence-governed, model-independent AI infrastructure. It converts the field definition in Paper 1 and the formal model in Paper 2 into externally observable behavioral requirements.

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The framework uses pre-registered evidence sets, policy profiles, declared system state, controlled perturbations, reference outcomes, negative controls and retained adjudication records. Nine profiles cover identity persistence, entity disambiguation, relationship invariance, material-change response, contradiction handling, repeatability, multi-node outcome equivalence, disconnected continuity and retrieval-baseline differentiation.

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Individual profiles use MEC-1 through MEC-9. Aggregate reporting uses MEC-C0 through MEC-C4 to avoid identifier collision. Conformance is reported as a capability vector and evaluator-independence class rather than as consciousness, general intelligence or universal understanding.

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The framework is implementation-neutral. It can evaluate proprietary, open, symbolic, geometric, graph-based, model-based and hybrid systems without requiring mandatory disclosure of protected internal mechanisms.

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The paper also specifies public formats for test cases, evidence records, adjudication records and conformance reports, together with a proposed synthetic pilot benchmark. Publication of the benchmark design does not constitute an executed conformance result.

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This is Paper 3 of the Geometry Intelligence Foundational Series. Paper 1 defines the field, Paper 2 defines governed relational transformation, Paper 3 defines external conformance, and Paper 4 develops an optional sovereign Web4 federation architecture.

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Final Manuscript 1.0. Published by KTS Global. Licensed under CC BY 4.0.

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