Object hallucination remains a major challenge in Multimodal Large Language Models (MLLMs), even though they have achieved strong performance across widely vision-language tasks. A key reason for object hallucination is limited fine-grained visual grounding provided by fixed-resolution visual encoding. In this paper, we propose Visual Inference Gating for Object Refinement (VIGOR), a training-free inference-time framework for binary object verification. VIGOR adopts a selective coarse-to-fine pipeline that improve visual grounding without the overhead of always-on multi-view inspection. It first uses the global yes–no margin to estimate whether the available evidence is sufficient. When the evidence is insufficient, VIGOR performs local refinement on uncertain cases using a small set of overlapping crops. Finally, VIGOR combines global and local evidence with an asymmetric decision rule. Experiments on three benchmark datasets across five MLLM backbones show that VIGOR consistently outperforms strong training-free baselines, recovering missed fine-grained evidence while maintaining robust performance on challenging visual reasoning.