用拼图测试视觉-几何推理，发现主流模型缺乏几何理解能力。
JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles
- 设计带凹凸结构的拼图块，结合视觉与几何约束确保答案唯一。
- 零样本下仅1个模型在4×4拼图上超随机水平，大网格性能暴跌。
- 揭示当前模型在复杂几何推理上的瓶颈，适合关注多模态认知的学者。
拼图求解需同时处理视觉内容与几何约束，但现有基准使用矩形切割，导致纹理重复区域存在模糊真值。我们提出 extit{ ous{}}，采用带凹槽与空缺的互锁拼图块，几何约束提供强局部兼容性要求，结合视觉信息可获得无歧义真值。在95,000个实例、四种网格密度（4×4至16×16）上，发现零样本视觉-语言模型（VLMs）普遍缺乏几何推理能力：五种前沿模型中仅一个（GPT-5.5）在4×4拼图上超过随机基线，其余均处于随机水平。尽管监督微调可在4×4达到97%以上准确率，但所有模型在更大网格上崩溃：GPT-5.5从70%降至近随机水平（8×8），即使微调模型在12×12上也低于5%。这一‘缩放悬崖’表明当前架构无法随拼图块数增加维持一致的约束满足。 extit{ ous{}}确立了可扩展几何推理作为视觉-语言模型的一项开放挑战。
原文摘要 · Abstract (English)
Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that create ambiguous ground truth in texture-repeated regions. We introduce \textit{\ours{}}, a benchmark with tab-and-blank interlocking pieces where geometric constraints provide strong local compatibility requirements that, combined with visual content, yield unambiguous ground truth. Across 95K instances at four grid densities (4$\times$4 to 16$\times$16), we find that \textbf{zero-shot VLMs largely lack geometric reasoning}: only one of five frontier models (GPT-5.5) exceeds random baseline on 4$\times$4 puzzles, while all others perform at chance level. While supervised fine-tuning achieves $>$97\% on 4$\times$4, \textbf{all models collapse on larger grids}: GPT-5.5 drops from 70\% to near-random on 8$\times$8, and even fine-tuned models fall below 5\% on 12$\times$12. This ``scaling cliff'' suggests current architectures cannot maintain consistent constraint satisfaction as the number of pieces increases. \ours{} establishes scalable geometric reasoning as an open challenge for vision-language models.
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