通过闭环视觉基础验证弥合自我反思中的模态脱节
Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification
AI总结：
提出MIRROR框架，通过闭环视觉反思（草稿-批评-区域验证-修订）减少VLM幻觉，并构建ReflectV数据集训练视觉基础的多轮反思。
摘要：
在视觉语言模型（VLM）时代，增强多模态推理能力仍然是一个关键挑战，尤其是在处理模糊或复杂的视觉输入时，初始推理常常导致幻觉或逻辑错误。现有的VLM通常产生看似合理但缺乏依据的答案，即使提示其“反思”，修正也可能与图像证据脱节。为了解决这个问题，我们提出了MIRROR框架，用于通过视觉区域的反思进行多模态迭代推理。通过将视觉反思嵌入为核心机制，MIRROR被表述为一个闭环过程，包括草稿、批评、基于区域的验证和修订，重复进行直到输出具有视觉基础。为了促进该模型的训练，我们构建了**ReflectV**，一个用于多轮监督的视觉反思数据集，明确包含反思触发器、基于区域的验证动作以及基于视觉证据的答案修订。在通用视觉语言基准和代表性视觉语言推理基准上的实验表明，MIRROR提高了正确性并减少了视觉幻觉，证明了将反思训练为一种寻求证据、区域感知的验证过程而非纯文本修订步骤的价值。
英文摘要：
In the era of Vision-Language Models (VLMs), enhancing multimodal reasoning capabilities remains a critical challenge, particularly in handling ambiguous or complex visual inputs, where initial inferences often lead to hallucinations or logic errors. Existing VLMs often produce plausible yet ungrounded answers, and even when prompted to "reflect", their corrections may remain detached from the image evidence. To address this, we propose the MIRROR framework for Multimodal Iterative Reasoning via Reflection On visual Regions. By embedding visual reflection as a core mechanism, MIRROR is formulated as a closed-loop process comprising draft, critique, region-based verification, and revision, which are repeated until the output is visually grounded. To facilitate training of this model, we construct **ReflectV**, a visual reflective dataset for multi-turn supervision that explicitly contains reflection triggers, region-based verification actions, and answer revision grounded in visual evidence. Experiments on both general vision-language benchmarks and representative vision-language reasoning benchmarks show that MIRROR improves correctness and reduces visual hallucinations, demonstrating the value of training reflection as an evidence-seeking, region-aware verification process rather than a purely textual revision step.
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。
