Does anyone have a name for that subtle "Sameness" creeping into model outputs lately? [R]

I've been running a lot of comparative evals across recent model releases—both API and open-weight—and there's a pattern I can't unsee.

After a certain number of turns, or when you push into niche territory, the outputs start converging. Same cadence. Same hedging phrases. Same blind spots. It's not full collapse. It's a kind of... homogenization. A creep.

My working theory: we're deep enough into the synthetic data flywheel now that we're seeing the first-generation effects. Not model collapse in the catastrophic sense, but a gradual loss of "texture" across models that share overlapping synthetic ancestry.

I've been calling this EchoCreep in my notes. The slow, creeping homogenization of model behavior driven by shared synthetic data lineage.

Has anyone else been tracking this? Is there a formal term yet? If not, what are you seeing in your evals that fits this pattern? I'm especially interested in:

• Concrete eval metrics that might capture it
• Whether fine-tuning on entirely human-curated data clears it
• If you've seen it worsen between checkpoint versions

any feedback would be appreciated?

Thanks