揭示细粒度视觉痕迹：评估多模态交错推理链在多模态STEM任务中的表现
Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks
AI总结：
本文提出StepSTEM基准，通过严格筛选的283个STEM问题，评估多模态大语言模型的跨模态推理能力，发现现有模型仍依赖文本推理，揭示了真实跨模态STEM推理的潜力。
摘要：
多模态大语言模型（MLLMs）在推理能力上表现出色，但在专业领域评估仍具挑战性。STEM推理是重要测试平台，因其反馈高度可验证，但现有基准常允许单模态捷径，仅关注最终答案准确性。为此，我们引入StepSTEM：一个包含数学、物理、化学、生物和工程283个问题的研究生级基准，用于细粒度评估MLLMs的跨模态推理。StepSTEM通过严格筛选流程确保文本与视觉输入的严格互补性。我们进一步提出通用的步骤级评估框架，用于文本-only链式思维和交错图像-文本推理，利用动态规划对齐预测推理步骤与多个参考解决方案。实验显示，当前MLLMs仍严重依赖文本推理，即使Gemini 3.1 Pro和Claude Opus 4.6也仅达到38.29%的准确率。这些结果突显了真实跨模态STEM推理的潜力，并将StepSTEM定位为细粒度多模态推理评估的基准。源代码可在https://github.com/lll-hhh/STEPSTEM获取。
英文摘要：
Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains challenging. STEM reasoning is a particularly valuable testbed because it provides highly verifiable feedback, but existing benchmarks often permit unimodal shortcuts due to modality redundancy and focus mainly on final-answer accuracy, overlooking the reasoning process itself. To address this challenge, we introduce StepSTEM: a graduate-level benchmark of 283 problems across mathematics, physics, chemistry, biology, and engineering for fine-grained evaluation of cross-modal reasoning in MLLMs. StepSTEM is constructed through a rigorous curation pipeline that enforces strict complementarity between textual and visual inputs. We further propose a general step-level evaluation framework for both text-only chain-of-thought and interleaved image-text reasoning, using dynamic programming to align predicted reasoning steps with multiple reference solutions. Experiments across a wide range of models show that current MLLMs still rely heavily on textual reasoning, with even Gemini 3.1 Pro and Claude Opus 4.6 achieving only 38.29% accuracy. These results highlight substantial headroom for genuine cross-modal STEM reasoning and position StepSTEM as a benchmark for fine-grained evaluation of multimodal reasoning. Source code is available at https://github.com/lll-hhh/STEPSTEM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
