Evidence Boundaries and Benchmark Design for Traceable Graph, Multimodal, And Systems Methods For Interdisciplinary Decision Support: With Multimodal And Relational Evidence in Operational Assurance
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Keywords

artificial intelligence
systems
universal
evidence synthesis
reliability
provenance
revalidation

Abstract

This critical review examines traceable graph, multimodal, and systems methods for interdisciplinary decision support. It asks which evidence boundaries and comparators are needed before a cross-domain model can support action. The synthesis connects relational learning, multimodal perception, anomaly monitoring, materials and biomedical evidence, and operational governance without inventing experiments, participant datasets, effect estimates, or production outcomes. Ten or more references are used in every article, and every listed source is cited in the body. Particular attention is given to method transfer proceeding without an explicit validity bridge. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.

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