Transformative Mirroring: Constrained Divergence, Correction, and Theoretical Framing in Human–AI

Abstract

Transformative mirroring is a proposed human–AI interaction procedure in which a receiver reconstructs a source’s communicated perspective through its own organization, returns that reconstruction for correction, checks externally assessable claims against independent references, and carries justified revisions forward. The framework distinguishes perspective fidelity from factual validity and transformation from capture, emphasizing preservation of the provenance and trajectory of change rather than preservation of an unchanged starting position. The manuscript proposes controlled model benchmarks, human–AI collaboration studies, component-removal experiments, divergence × continuity tests, and transfer conditions to evaluate whether transformative mirroring improves collaboration beyond active revision controls. The work originated within Observer-Embedded Reality (OER), but its central procedure is designed to be empirically testable without requiring adoption of OER terminology. No preregistration, data collection, or experimental results are claimed.