基于链式思维的多模态大语言模型视觉空间推理能力退化
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
AI总结：
研究发现链式思维范式在视觉空间推理任务中导致性能下降，通过实验揭示多模态模型在空间基准测试中的关键缺陷，并指出需转向以视觉为中心的推理方法。
摘要：
多模态推理模型（MRMs）利用链式思维（CoT）进行推理已革新了数学和逻辑问题解决。然而，我们发现这种范式在通用空间智能方面存在困难。我们对十七个模型进行了十三个空间基准测试的全面评估，并发现了一个关键缺口：CoT提示在视觉空间推理中持续降低性能。此外，通过新的No-Image++消融实验，我们证明MRMs和CoT提示的MLMs存在严重的捷径学习，并在图像缺失时从文本先验中虚构视觉细节。这些发现挑战了纯文本CoT在空间任务中的有效性，并强调了需要以视觉为中心的推理范式。
英文摘要：
Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradigm struggles with generalized spatial intelligence. We perform a comprehensive evaluation of seventeen models across thirteen spatial benchmarks and identify a critical gap: CoT prompting consistently degrades performance in visual spatial reasoning. Furthermore, through a novel No-Image++ ablation, we demonstrate that MRMs and CoT prompted MLMs suffer from severe shortcut learning, and hallucinate visual details from textual priors even when the image is absent. These findings challenge the efficacy of text-only CoT for spatial tasks and underscore the need for vision-centric reasoning paradigms.
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