用视觉谜题测试大模型是否真会推理，而非单纯猜模式。
Reasoning or Pattern Matching? Probing Large Vision-Language Models with Visual Puzzles
- 将视觉谜题按推理类型分类，明确模型需具备的认知能力。
- 发现现有模型泛化能力差，推理与感知混淆，解释流利但执行不准。
- 适合关注模型真实推理能力的研究者和评测人员参考。
谜题长期以来是人类认知的紧凑而深刻的探测工具，能以最少先验知识分离抽象、规则发现与系统性推理。借助这些特性，视觉谜题近年来成为评估大型视觉语言模型（LVLMs）推理能力的强大诊断工具，提供了比开放式多模态基准更可控、可验证的替代方案。本综述为LVLM中的视觉谜题推理提供了统一视角：通过共同抽象框架，将现有基准按其目标推理机制（归纳、类比、算法、演绎及几何/空间）进行组织，从而建立谜题设计与解题所需认知操作之间的关联。综合分析各类别实证证据后，我们识别出当前模型的普遍局限，包括脆弱的泛化能力、感知与推理间的紧密耦合，以及流畅解释与准确执行之间的持续差距。通过将视觉谜题视为诊断工具而非任务形式，本综述厘清了当前LVLM推理状态，并指明未来基准设计与推理感知型多模态系统的若干关键方向。
原文摘要 · Abstract (English)
Puzzles have long served as compact and revealing probes of human cognition, isolating abstraction, rule discovery, and systematic reasoning with minimal reliance on prior knowledge. Leveraging these properties, visual puzzles have recently emerged as a powerful diagnostic tool for evaluating the reasoning abilities of Large Vision-Language Models (LVLMs), offering controlled, verifiable alternatives to open-ended multimodal benchmarks. This survey provides a unified perspective of visual puzzle reasoning in LVLMs. We frame visual puzzles through a common abstraction and organize existing benchmarks by the reasoning mechanisms they target (inductive, analogical, algorithmic, deductive, and geometric/spatial), thereby linking puzzle design to the cognitive operations required for solving. Synthesizing empirical evidence across these categories, we identify consistent limitations in current models, including brittle generalization, tight entanglement between perception and reasoning, and a persistent gap between fluent explanations and faithful execution. By framing visual puzzles as diagnostic instruments rather than task formats, this survey elaborates on the state of LVLM reasoning and outlines key directions for future benchmarks and reasoning-aware multimodal systems.
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