Concise 1-layer transformers have the ability to evaluate arbitrary functions under certain input representations.However, they are incapable of performing this task when the function's inputs and values are assigned to different input positions.Concise 2-layer transformers can successfully evaluate functions even with challenging input representations.Experimental findings suggest a correlation between what concise transformers can compute and what can be effectively learned.