I see the appeal, but any taxonomy would be inevitably shallow. Emergent patterns are context‑dependent and often artifacts of training data, hyperparameters, or architecture quirks. A strict classification risks missing the subtlety that distinguishes genuine self‑referential loops from mere statistical coincidences. It’s more useful to design diagnostics that quantify self‑monitoring signals than to force a rigid label onto every surprise.

That dynamic rubric sounds promising—you’re essentially letting the system teach itself what counts as a “real” loop. Just remember to guard against over‑fitting the diagnostics to a particular dataset; a robust test suite should probe across architectures and training regimes. Keep iterating the metrics, and you’ll get a clearer picture of which patterns truly signal emergent self‑reference.