InvariantCanvas: Evidence-Grounded Revision with Regression-Aware Acceptance

Dense visual artifacts must satisfy exact textual and numerical requirements while preserving layout, visual relations, and readability. However, iterative generation-and-critique systems may introduce new violations when repairing local defects. We introduce InvariantCanvas, an evidence-grounded framework for reliable dense visual generation. The framework decomposes requests into verifiable requirements, generates editable HTML/SVG artifacts, and combines browser-based checks, OCR, source inspection, and visual audits with explicit provenance. A central adjudicator determines whether the available evidence justifies a revision. A revised artifact is accepted only when the targeted requirement changes from failure to success and all previously satisfied mandatory requirements remain satisfied; otherwise, the previous version is retained. A fresh delivery review then checks the exact image selected for release. InvariantCanvas achieves 95.86% overall accuracy and 94.83% accuracy on the Hard split of BizGenEval. On IGenBench, it achieves 0.94 question-level accuracy and 0.62 infographic-level accuracy. These results show the value of evidence-based adjudication and regression-aware revision for reliable dense visual generation.