model collapse
Model collapse is the slow-motion degradation that happens when AI models train on AI-generated data. As synthetic media floods the web, future models risk losing diversity, accuracy, and quality in a compounding feedback loop.
data provenance
OriginBlame introduces a technique for tracing data provenance at both the record and token level in AI training datasets, offering new tools for accountability, attribution, and authenticity in the era of generative models.
model collapse
A new arXiv paper frames model collapse from synthetic data contamination as an epidemiological problem, applying bilayer SIR dynamics to model how AI-generated content spreads through training corpora and degrades future models.
data provenance
New research proposes a Compliance Rating Scheme that evaluates generative AI datasets for licensing, consent, and ethical sourcing—critical infrastructure for accountable synthetic media.