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English(EN) Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning

新基准揭示了MLLM视觉推理能力的局限性

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员正在开发新的基准和方法来评估多模态大型语言模型（MLLM）的视觉推理能力。一个名为“The Prestige”的新基准使用魔术来挑战模型对时间推理和因果推理的理解，揭示了当前模型与人类表现之间存在的巨大差距。另一种方法 ReGap-Bench 表明，多步视觉推理的失败源于“参考差距”，即模型难以将语言与特定的视觉元素联系起来，并且外部参考可以在不重新训练的情况下提高准确性。此外，一项关于“视觉思维忠实度”的研究发现，即使最终答案正确，MLLM在推理链中也经常生成不准确的视觉信息，这促使人们开发提高多模态思维链推理中视觉组件的准确性和相关性的方法。 AI

影响 强调了当前多模态模型的局限性，并为改进其视觉推理和忠实度指明了新方向。

排序理由 多篇研究论文介绍了多模态大型语言模型的新基准和评估方法。

在 Hugging Face Daily Papers 阅读 →

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新基准揭示了MLLM视觉推理能力的局限性

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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Shuo Wen, Beixi Du, Edwin Meriaux, Junming Shi, Chloe Si, David Meger, Doina Precup, Gregory Dudek ·

    声望：使用魔术技巧进行认知视觉推理的基准测试

    arXiv:2610.38103v1 Announce Type: cross Abstract: We introduce The Prestige, a video understanding benchmark that probes cognitive visual reasoning through professional magic performances, where deliberate misdirection challenges models to track events and explain unexpected outc…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Diagnosing the Reference Gap: What Visual Scaffolds Can and Cannot Fix in Multimodal Reasoning

    Multimodal language models fail at multi-step visual reasoning due to a reference gap rather than perception, and task-matched external references like numbering significantly improve accuracy without training.

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    What do MLLMs know about 'Waldo'? Evaluating Visual Reasoning under Multi-Instance Multi-Object Challenges

    Recent advances in Multimodal Large Language Models (MLLMs) have led to significant progress in visual reasoning capabilities. However, existing benchmarks curate image-question pairs pertaining to object categories with few instances, enabling trivial object referencing and limi…

  4. arXiv cs.CV TIER_1 English(EN) · Zujing Liu, Junwen Pan, Qi She, Yuan Gao, Guisong Xia ·

    On the Faithfulness of Visual Thinking: Measurement and Enhancement

    arXiv:2510.23482v2 Announce Type: replace Abstract: Recent large vision-language models (LVLMs) can generate vision-text multimodal chain-of-thought (MCoT) traces after reinforcement fine-tuning (RFT). However, we observe that the visual information in MCoT is often inaccurate ev…

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