Object hallucination in Multimodal Large Language Models (MLLMs) often appears in two forms: omitting objects that are present in the image and generating objects that are absent. Existing training-free mitigation methods usually apply a uniform intervention to both cases. We find that this strategy leads to an imbalanced effect: it can reduce some omission errors, but often introduces new fabrications. To understand this behavior, we analyze spatial attention patterns and layer-wise attention dynamics during generation. Our results suggest that omissions often occur when the model attends to the relevant visual regions but gradually loses visual confidence during decoding, whereas fabrications are more closely related to deep-layer semantic priors overriding visual evidence. Based on this observation, we propose Visual Potential Field Calibration (VPFC), a training-free hidden-state calibration method. VPFC locates an intervention region using the spatial centroid of high-confidence visual tokens and applies a lightweight hidden-state update to adjust the model’s generative confidence. Experiments on multiple hallucination benchmarks show that VPFC consistently improves over existing training-free methods while maintaining inference efficiency close to standard greedy decoding.