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According to @emollick referencing @JustinWaugh’s release, the Pencil Puzzle Bench tests 51 LLMs on 62k unique pencil puzzles across 94 types with an evaluation set of 300 puzzles over 20 types, showing modern reasoner models dramatically outperform early non‑reasoner LLMs. As reported by @JustinWaugh, the best score is 56% by GPT 5.2 at xhigh settings, and roughly half the puzzles remain unsolved, highlighting significant headroom for tool‑supported reasoning and verification‑driven training. According to the X thread by @JustinWaugh, the benchmark emphasizes multi‑step logical reasoning with step‑verifiable solutions, providing a clearer signal for chain‑of‑thought robustness and planning. As noted by @emollick, performance gains appear logistic due to a 100‑point ceiling, suggesting maturing returns and the need for targeted data curricula, planner‑solver architectures, and self‑verification loops for enterprise use cases like operations optimization, scheduling, and compliance workflows.