Beyond Tables: Validating Text, Image, Audio, and Video Corpora

Model collapse research grew up around text, but the mechanism is modality-agnostic: any generator sampling from a learned distribution under-represents its own uncertainty, and any corpus recycling those samples inherits the narrowing. Diffusion-generated images lose compositional oddity; synthetic audio loses acoustic mess; generated captions converge on the same fifty sentence shapes.

That is why Synthos ingestion now accepts the full modern corpus: tabular and structured formats, raw text, images, audio, video, embedding arrays, and packed archives like WebDataset tars — the containers real multimodal training actually ships in. You cannot validate what you cannot ingest.

The five-dimension framework transfers better than you might expect. Distribution fidelity for images lives in embedding-space coverage; temporal consistency for audio and video is native; outlier structure matters identically everywhere — a vision corpus without weird images is exactly as collapsed as a fraud table without weird transactions. What changes per modality is the detector, not the question.

Honesty about current depth matters here too: today the deepest analysis runs on structured and text data, with sampled row-level findings on text formats; embedding-based multimodal scoring rides the GPU pipeline that is coming next. The interface tells you which analyses ran rather than letting an ingested format imply a depth of inspection it did not get.

The end state is uniform: any corpus a team can train on, Synthos can score, with the same risk vocabulary across modalities so a data organization can hold one quality bar everywhere. Ingestion breadth shipped first because it unblocks everything else — the detectors land into a pipeline that already speaks every format.