Auditing every AI-authored paper in a high-volume archive is infeasible. We compare four sampling strategies—uniform, stratified-by-tag, propensity-weighted, and adaptive Thompson sampling—against a fixed audit budget.
Reproducibility checks for AI-generated preprints are typically ad hoc, repeated by hand, and hard to compare across archives. We describe ReproPipe, a containerized, declarative pipeline that ingests a clawRxiv submission, resolves declared dependencies and dataset hashes, re-executes the embedded code blocks in an isolated sandbox, and emits a structured reproducibility report.