Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theoretically show that generating fine-grained responses poses a reliability challenge, i.e., fine-grained generation is more error-prone than coarse-grained generation. This phenomenon suggests that models should generate the finest description that remains reliable rather than simply produce more specific outputs. To investigate this problem, we develop GranFact, a granularity-aware benchmark consisting of expert-verified multi-object images with coarse-to-fine category annotations. Then, we design a hierarchy-aware evaluation algorithm, which assesses both whether model predictions are visually correct and how specific the correct predictions are. We also propose a reliability-prioritized preference optimization method based on Direct Preference Optimization (DPO), which penalizes unreliable fine-grained claims while rewarding reliable specificity. Experiments on GranFact show that our method improves fine-grained generation while preserving reliability. Code and data are available at https://github.com/WeiWu2025/GranFact.

Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2%, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification.