Revision-Consistent AI Reporting
A proposed framework for updating only the financial-report claims affected by revised evidence while preserving valid findings.
Status: Research ideation and experimental design. No empirical model results are claimed yet.
How can AI-generated reports stay accurate when their underlying numbers change?
This ongoing research investigates versioned evidence, claim dependencies, selective narrative revision, and deterministic validation. The first pilot is designed around 20 synthetic report packages with four controlled revisions each, comparing five approaches using correction accuracy, factual stability, and unsupported-claim metrics.
Explore the interactive revision simulator →
