Comprehension as Compliance: How Grounded Content, Not Form, Moves Small Language Models to Act, and Where It Does Not

A small language model, given a task prefixed with a description of a recurring decision, will often recognize the decision and fail to act on it. This paper draws together a program of studies on that effect and states what drives it. The effect is carried by the content of the description, not by its form: a plain imperative rule that carries the same information suppresses action as much as the descriptive form, so the form is not the lever. What recovers action is grounding the decision in the concrete domain; a description that names the concrete situation but withholds the correct action recovers most of the lift, so the grounding, not being told the answer, is what matters. Recognition without action is also, in part, an artifact of a single-turn test: given a minimal tool loop, the model carries out decisions it had only described, for classes whose correct action is to act on a known target; for classes whose correct action is to assemble a result from several inputs, the gap can survive the loop. Across the set of decision classes we have distilled into such descriptions and screened for quality, comprehension of the content is the load-bearing ingredient in three of the six that admit clean controls, weakly indicated in a fourth and fifth, with one class mixed across models and its cleanest cell carried by the mere presence of a long prefix rather than its content. That set is small and drawn from a larger, bounded backlog of candidate cases, so the distribution is an indication to test against a fuller set, not a census. All subjects are open-weight models on one local raw-completion rig, reproducible on a single 24 GB GPU with no paid interface.