Cross-modal (research preview)
A research-preview surface that targets content where each individual channel reads benign but the combination is not. Evasion increasingly hides in the seams between modalities; cross-modal correlation analysis is on the v1.1 roadmap, building on AEGIS's 6-axis dual-judge rubric.
What it would process
The future detector targets content where each individual channel reads benign but the combination is not. Evasion increasingly hides in the seams between modalities; this surface is being built toward joint reasoning across channels.
Adversarial patterns it is tuned to catch
- Audio-visual disagreement (clean image, hostile narration)
- Caption-to-image inversion (caption flips the image's meaning)
- Metadata-content disagreement (envelope says one thing, body another)
- Layered evasion that requires understanding two modalities at once
Contribution to the ensemble verdict
Outputs both a cross-modal disagreement score and an evidence trace identifying which channels disagreed.
Per-detector outputs are not a final verdict. In v1.0 the analyze pipeline produces verdicts from the 6-axis dual-judge rubric; cross-modal scores will join the rubric in v1.1 once the research preview promotes to production.