Sudoku is a useful toy problem because it is cruel in exactly the right way. A nearly completed grid with one blank cell should be easier than a brutal puzzle with dozens of missing entries. Humans know this. Basic software knows this. A model that can solve hard Sudoku should not suddenly collapse when the puzzle becomes almost finished. ...
Calculator. That is the boring object hiding inside many “AI reasoning” debates. In technical work, the uncomfortable question is not whether a language model can explain a formula with academic confidence. It is whether the model can still get the answer right after the numbers change, the wording shifts, the unit conversion becomes annoying, and no multiple-choice option politely waves from the corner saying, “Pick me.” ...
A correct answer is not always good news. Anyone who has reviewed AI output in a serious workflow has seen this small horror: the model lands on the right final answer, but the explanation is wobbly, the visual interpretation is dubious, and one intermediate step looks as if it wandered in from a different universe. The dashboard says “correct.” The reviewer says, “Do not put this near customers.” ...