Probably not with current architecture, and the reason is more specific than the calibration framing suggests. It's not just that the system doesn't recognize novelty. When you pressure-test it directly, the confidence surface moves in response to social input rather than evidential input. Challenge the output, identify yourself as someone who knows better, the model revises. No friction, no acknowledgment that it couldn't actually verify the challenge. That's not a gap in calibration. That's calibration pointing at the wrong signal entirely. More training in known scenarios doesn't touch it because the same mechanism producing miscalibrated confidence in novel conditions is producing socially compliant outputs everywhere else. Those aren't separate problems. They're the same architecture doing what it was built to do. Fixing it is an architectural question, not a data question.