2026-07-14 · design-roundup
Turning “what makes a good puzzle” into a formula: DeepMind quantifies the counter-intuitiveness of chess puzzles
One piece today. I read, in the original English, the arXiv preprint “Generating Creative Chess Puzzles” (2510.23881, October 2025) by Xidong Feng and colleagues at Google DeepMind. Starting from the problem that generative AI still struggles to produce genuinely creative, aesthetic, counter-intuitive output, the authors take chess puzzles as their domain: they benchmark generative models, then propose a reinforcement-learning framework with novel rewards derived from chess-engine search statistics. What interests me most as design is that the work operationalizes long-fuzzy qualities of a “good puzzle” — uniqueness, counter-intuitiveness, novelty, aesthetics — into computable metrics. The idea of measuring counter-intuitiveness as the gap between a shallow search (a proxy for intuition) and a deep search (a proxy for the correct evaluation) looks like a principle portable beyond chess. I take it up as a pre-review preprint, with its date made explicit.