narrative-scene-qa

You can verify narrative-image fidelity by comparing the rendered output against the beat's title, body, and prior canon. This flags faithfulness failures and returns a deterministic JSON payload for automated downstream correction.

Narrative continuity checking is a post-render evaluation that compares visual output against canonical beat data. It flags faithfulness failures between the image and the scene intent to ensure strict visual alignment.

You get automated JSON feedback by validating the rendered image prompt with canonical beat context. The evaluation returns a strict JSON payload containing approved status, score, issues, and fix_hint without any prose.

Yes, you can use this for automated post-render image evaluation because it operates safely without prose or tool calls. It provides deterministic JSON verdicts specifically designed for integration into automated rendering pipelines.

The best way to flag canon inconsistencies is to compare the rendered narrative image against the canonical beat data. This process identifies faithfulness failures and returns a structured JSON verdict with a fix_hint.

Your narrative image evaluation returns a strict JSON payload to ensure safe, prose-free evaluation suitable for automated downstream processing. This deterministic format includes approved, score, issues, and fix_hint fields for automated re-rendering.