We present our system for the SHROOM-Visions 2026 shared task on fine-grained hallucination detection in vision-language model (VLM) outputs. Our approach uses three token-level internal signals from Qwen2.5-VL-3B-Instruct: hidden representations, the change in token confidence when the image is removed, and predictive uncertainty. A lightweight classifier combines these signals to predict hallucinated spans and their confidence, while LoRA enables parameter-efficient adaptation of the VLM backbone. Our submission achieves IoU scores of 0.36–0.42 and Spearman correlations of 0.41–0.46 on English, French, and Italian, ranking 10th in both French and Italian. We additionally analyze continued-training experiments that improved our local validation scores but consistently degraded official test performance. Our analysis attributes this discrepancy primarily to threshold tuning on a small validation split, which was only partially mitigated by subsequent adjustments. Increasing the backbone size from 3B to 7B parameters did not resolve the issue. These findings highlight the sensitivity of hallucination detection systems to validation size and decision-threshold selection.