When a language model invents a fact, the first question is obvious: what is true?

The second arrives wearing a lab coat: why did the model do it?

Ask the machine that just failed and it may provide a plausible, technical cause, neatly shaped like closure. It may also be a second hallucination.

We distrust unsupported answers. We are less disciplined with unsupported post-mortems.

A cause is another claim

A model says that it confused two names because they appeared near each other in training. Or that a tool call failed because a rate limit was reached. Or that missing context caused it to infer the wrong event. Each explanation has the texture of mechanism.