轻看，重思：多模态思维链推理能做什么和不能做什么
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
AI总结：
本文系统探究多模态思维链推理在12个感知与推理任务上的表现，发现CoT对感知任务有副作用，但对数学、科学等多图像推理有效，且现有开源多模态推理模型提升有限，视觉推理仍是瓶颈。
摘要：
思维链（CoT）已成为通过引发逐步思考来提高大型语言模型（LLMs）推理能力的标准方法，但其在多模态任务中的有效性仍不清楚。本文旨在系统探究关键问题：多模态思维链推理能做什么，它在哪些方面以及为什么表现不足？为此，我们使用14个非推理模型和8个推理模型，评估了跨感知和推理类别的12个多模态任务。我们的分析揭示了几个重要发现：（1）CoT并非免费的午餐，应根据每个任务的具体要求有选择地使用。对于感知任务，CoT可能导致不良副作用，如视觉定位和物体计数性能下降。相反，它对于涉及数学、科学和多图像推理的推理任务有效；（2）与原始模型相比，现有的开源多模态推理模型通常只带来微小的整体改进，可能是由于过度强调数学推理而牺牲了更广泛的能力；（3）视觉推理仍然是当前多模态CoT的关键瓶颈，因为模型表现出“轻看，重思”的模式，其中言语反思在推理过程中起伏，而视觉反思持续减弱。这些发现表明，虽然多模态CoT相对较好地处理了言语反思，但它缺乏在整个推理过程中保持深度视觉内省的能力。
英文摘要：
Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness in multimodal tasks remains unclear. In this paper, we aim to systematically investigate the key question: What can multimodal Chain-of-Thought reasoning do, and where and why does it fall short? To this end, we evaluate 12 multimodal tasks across perception and reasoning categories using both 14 non-reasoning models and 8 reasoning models. Our analysis reveals several important findings: (1) CoT is not a free lunch and should be used selectively depending on the specific requirements of each task. For perception tasks, CoT can lead to undesirable side effects, such as reduced performance in visual grounding and object counting. In contrast, it proves effective for reasoning tasks involving mathematical, scientific, and multi-image reasoning; (2) Compared to original models, existing open-source multimodal reasoning models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities; (3) Visual reasoning remains a key bottleneck for current multimodal CoT, as models exhibit a Look Light, Think Heavy pattern where verbal reflection rises and falls during reasoning, whereas visual reflection consistently diminishes. These findings suggest that while multimodal CoT handles verbal reflection relatively well, it lacks the ability to maintain deep visual introspection throughout the reasoning process.
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