While reviewing model outputs across different contexts, I keep running into a pattern that’s easy to miss if we focus only on accuracy.
In many cases, the outputs are internally consistent and factually acceptable, yet grounded in a narrower context than the one actually lived.
What disappears isn’t correctness, but representation.
The model seems to converge on the most stable and compressible framing, while other valid contexts fade — not because they’re wrong, but because they’re harder to represent.
I’m curious how others here think about evaluating this kind of gap.
Not “is this wrong?”, but “which context is being allowed to speak?”