We describe our submission to SHROOM-Visions 2026, the shared task on generator-agnostic, character-level hallucination span detection and typing in vision-language model outputs across English, French, Italian, and Chinese. Rather than training a detector, we use a crowd-emulating judge ensemble: one instruction-tuned multimodal model, sampled at several temperatures and with several prompt variants, independently re-inspects each image, prompt, and response and proposes typed hallucination spans. Character-level vote fractions form soft probabilities that emulate the multi-annotator gold labels, while a cross-validated threshold per language produces hard spans. The black-box system requires no parameter updates and relies on guideline-derived prompt rules and language-specific few-shot examples. On the official closed test set, it ranks first for all English metrics and for Italian IoU, and second for every remaining language-metric combination. Official scores range from 0.4336 to 0.4845 for Cor+Lbl, 0.5474 to 0.5814 for Cor, and 0.4865 to 0.5240 for IoU. On held-out training folds, IoU is 0.32 to 0.39 on non-empty items. Ablations identify prompt design and few-shot conditioning as decisive, while an added adjudication stage does not improve performance. Vote fractions track annotator agreement but remain globally over-confident (ECE 0.08 to 0.12), motivating per-language thresholding.

Large Language Models (LLMs) achieve strong performance on many tasks, but they still struggle with morphologically rich, low-resource languages such as Turkish. This difficulty stems from Turkish being an agglutinative language and underrepresented in multilingual training data, which causes current models to often fail at capturing its morphology, flexible word order, and formal registers. In this paper, we introduce MODA (Model Adapted for Domain Applications), a Turkish-specialized LLM built via a modular pipeline that combines continual pre-training, parameter-efficient fine-tuning, and model merging. Starting from Qwen2.5-7B as the base model, we first perform large-scale continual pre-training on a Turkish web corpus to improve grammatical and morphological representations. We then apply parameter-efficient supervised fine-tuning on task-oriented instruction data, and finally merge specialized variants into a single unified model. We evaluate MODA on TurkishMMLU, the Turkish subset of EXAMS, and TRCLAIM-19, where it consistently outperforms both the base and instruction-tuned Qwen2.5-7B models. Our results support a training strategy that explicitly separates linguistic acquisition from task alignment when adapting LLMs to morphologically rich, underrepresented languages under realistic hardware constraints.