Only the Story Completes the Grid: Hand-Authored and Grammar-Generated Puzzles for Narrative Augmented Reasoning

ARC-style benchmarks exclude language by design; language-annotated variants show a written description can stand in for visual examples. Neither tests the widely made claim that narrative scaffolds reasoning through novel problems. We introduce NARC, Narrative Augmented Reasoning Challenges: sequences of colour grids, one masked, with a short story as the clue. The story never mentions grids: it helps only when read figuratively, a character as a bar, a mood as its height. A puzzle has the NARC property for a model when neither channel alone yields the masked grid and both together do. We evaluate 633 puzzles on eight open-weight models with an ablation battery separating lexical, order and form dependence. In 69% of narrative-necessary cases the clue's keywords are not enough; grids sufficing is a puzzle defect for 2% and a model fact for 32%. A stronger closed model keeps the property on 47% of 226 human-authored puzzles, depending less on the story's form and more on its words. We then present the Narrative Hierarchy Model, a dual-surface grammar whose every event rewrites into a sentence and a grid transition. It generates 21,864 puzzles from TinyStories, the property certified by construction, plus a control text faithful to the grids; data and generator will be released. Stating the rendering conventions outright does not substitute for the story: what the models lack is the event chain, not the code. Models trained from scratch learn the control text from four to five times fewer puzzles than the story, and paraphrases with clean labels train like the story: the cost is wording, not labels. What a narrative contributes is event structure; what it costs a learner is the variety of the surface that carries it.