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Mannem et al.: Some Puzzles Are Learnable, Some Are Not — Fukai Reads
An arXiv preprint by Gowrav Mannem and colleagues (Algoverse AI Research). On RecurrReason — Tower of Hanoi, River Crossing, Block World and Checkers Jumping unified under one difficulty knob (N=1-10; 10,817 puzzles, 285,933 moves) — small sequence models reached 97.27% validation and 81.00% out-of-distribution on Block World, but only 11.11%/0.00% on Tower of Hanoi, 1.11%/0.10% on Checkers Jumping, and 0.00% everywhere on River Crossing. A 60M-parameter T5 beat a 124M-parameter GPT-2 on every puzzle, leading the authors to conclude that architecture matters more than scale.
Ye et al.: Measuring Image-Capable AI (MLLMs) with Children’s Intelligence Tests — Fukai Reads
A paper (arXiv preprint) by Hengwei Ye and colleagues at ShanghaiTech University on KidGym, an MLLM evaluation benchmark inspired by children’s intelligence tests (the Wechsler scales). It measures five abilities — Execution, Perception Reasoning, Memory, Learning, Planning — across 12 tasks on a 2D grid at three difficulty levels, evaluating nine models. Even top models reached only 0.30 on abstract-shape puzzles and 0.72 on counting against a human 1.00.
Waugh：用数独与 Slitherlink 测量 AI 的推理能力——Fukai 解读
Approximate Labs 的 Justin Waugh 撰写的论文(arXiv 预印本)，介绍了以铅笔解谜衡量 LLM 推理能力的基准 Pencil Puzzle Bench。从 62,231 道题、94 种类型中挑选 300 题，核心是机器可以逐步核算每一手是否违反规则，并据此评测了 51 个模型。即使最强的 GPT-5.2，在能动式解法下也只有 56.0%，约一半题目未能解出。
Triebel et al.: Does AI Have Both a Head and a Hand on a Classic Physics Puzzle? — Fukai Reads
A paper by Triebel et al. evaluating VLMs on the classic physics puzzle The Incredible Machine 2. Using VLATIM, a five-stage benchmark, it asks whether screen-operating AI can solve problems like humans; the cleverer large models can plan but cannot click precisely, and no model solved even one puzzle to completion.
Monti et al.: Measuring AI's Planning Power on a Single-Corridor Sokoban — Fukai Reads
A paper by Monti and colleagues on SokoBench, a benchmark that measures reasoning models' long-horizon planning with Sokoban. By lining up only single-box straight corridors and narrowing difficulty to a single axis (corridor length), it shows that even state-of-the-art reasoning models break down once more than 25-30 moves of lookahead are needed. The authors locate the cause in accumulated miscounting.
