Answers are grounded in this paper and cite their sources.
Working paper develops a framework to separate external results from local data structures, suggesting implications for AI outputs.
This working paper develops External Result Non-Substitution and Parallel Reference as a non-executable structural framework for AI+AGI outputs, external tool results, external analysis results, external code or document outputs, and local source structures. The paper addresses the risk that externally generated or externally assisted results may be mistaken for replacements of original documents, local source data, local structured records, human review records, state objects, seal objects, address objects, propagation objects, or institutional decisions. It defines external results as reference objects that may correspond to local source structures without modifying, deleting, replacing, overwriting, absorbing, or finalizing those source structures. The framework further positions multiple external results as parallel reference objects. Conflicting, divergent, partial, alternative, or provider-specific external outputs may be maintained side by side without being collapsed into a single authoritative result, selected as final, silently merged into the source, or converted into legal, evidentiary, institutional, or operational effect. The framework does not present an execution system, source-modification system, automated judgment system, state-generation system, evidence-confirmation system, approval system, deletion system, priority-selection system, or legal-effect mechanism. It provides a non-executable boundary for preserving the difference between external result reference and local source replacement. This working paper is Paper 24 of the AGI Structural Alignment Series.
No takes yet. Share an insight, caveat, or question.
The First Waters (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: