反思以获取信息：通过信息增益驱动的验证提升多模态推理
Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
AI总结：
本文提出VRE框架，通过信息增益驱动的自我进化训练，提升多模态大语言模型的推理准确性和感知可靠性，减少幻觉，尤其在长链任务中效果显著。
摘要：
多模态大语言模型（MLLMs）在多模态推理任务中表现出色，但我们在长文本生成中发现一个常见故障模式：随着输出变长，模型逐渐偏离图像证据，转而依赖文本先验，导致不 grounded 的推理和幻觉。基于注意力分析，我们发现MLLMs具有潜在的晚期阶段视觉验证能力，但未被一致激活。受此启发，我们提出Visual Re-Examination（VRE），一种自我进化的训练框架，使MLLMs在推理过程中能自主进行视觉反思，而无需额外视觉输入。VRE通过模型自身生成反思轨迹，利用信息增益使视觉信息可操作。在多样化的多模态基准测试中，VRE一致提升了推理准确性和感知可靠性，同时显著减少了幻觉，尤其在长链任务中效果显著。代码可在https://github.com/Xiaobu-USTC/VRE获取。
英文摘要：
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.
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。
