Tabular-Ai

TL;DR for operators Many tabular foundation models behave like very competent consultants with a mildly expensive habit: they want the entire labelled training set placed in front of them at inference time. That works neatly on small datasets. It becomes rather less charming when the table grows to tens or hundreds of thousands of rows and the model’s attention cost starts behaving like it has discovered compound interest. ...

A forecast can be wrong in many ways. It can miss by a little. It can miss by a lot. It can be accurate on average while quietly underestimating rare but expensive outcomes. It can give a beautifully low RMSE while assigning laughably thin probability to the event that later eats the budget. This is the sort of mistake that looks harmless in a dashboard and expensive in a board meeting. ...