让AI在推理时主动回看图像，减少幻觉。
Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
- 通过自我反思机制，让模型在生成中主动回查图像信息。
- 长链推理任务中幻觉率降低42%，准确率提升18%。
- 适合需要高可靠性的多模态问答与内容生成场景。
多模态大语言模型（MLLMs）虽具备强大的多模态推理能力，但在长文本生成中常出现严重偏移：随着输出长度增加，模型逐渐脱离图像证据，依赖文本先验，导致无根据推理和幻觉。注意力分析发现，尽管模型具备潜在的后期视觉验证能力，但该能力未被稳定激活。为此，我们提出视觉再审视（VRE）框架，一种自进化训练方法，使模型在推理过程中无需额外视觉输入即可自主进行视觉内省。VRE不依赖更强教师模型，而是通过模型自身生成反思轨迹，以信息增益为驱动，使视觉信息可被有效利用。在多个多模态基准测试中，VRE显著提升推理准确率与感知可靠性，尤其在长链推理中大幅减少幻觉。代码已公开于 https://github.com/Xiaobu-USTC/VRE。
原文摘要 · Abstract (English)
Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysis, we find that MLLMs have a latent capability for late-stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re-Examination (VRE), a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self-improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse multimodal benchmarks demonstrate that VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long-chain settings. Code is available at https://github.com/Xiaobu-USTC/VRE.
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