WiNCC is a reproducible and automatic construction pipeline for building an English–Nepali cross-lingual retrieval corpus that is difficulty-aware, relying on public Wikipedia and Wikidata. Part 1 reproduces a CLIRMatrix-style baseline. Nepali Wikipedia was crawled, articles resolved to a Wikidata QID, then intersected with Wikidata sitelinks to obtain Nepali–English query pairs and the corresponding English document corpus. These were sampled into gold pairs and padded with random English distractors to form a document baseline corpus. Part 2 adds a data-enrichment stage to Part 1 with multi-query, class-matched hard negatives. We compare the produced corpus against existing cross-lingual retrieval resources, run cross-lingual retrieval baselines with three distinct multilingual embedding models (BGE-M3, multilingual-E5-large, and Qwen3-Embedding-4B), and analyze slice-wise quality metadata and query-type-wise field metrics supporting the corpus’s use as a difficulty-aware benchmark. Through our corpus, we also find (1) a 27.8–41.5-point NDCG@10 drop from English to Nepali, (2) descriptions are 5–6× harder than titles, and (3) hard negatives are 2× stickier than random distractors.

Fine-grained large-vision-language hallucination detection presents the challenge of locating hallucinated content in image-grounded responses and classifying them with corresponding hallucination categories. Our method for the SHROOM-Visions 2026 Shared Task explores a parameter-efficient solution by fine-tuning a lightweight token-level classification head on the frozen weights of the Qwen3-VL-8B-Instruct vision-language model. We utilized category-specific thresholds calculated based on validation character-level IoU and designed a span merging post-processing step to facilitate span localization with fewer fragmented predictions. We also establish a possible connection between the annotator agreement and the operating thresholds. Our analysis suggests that, within our experimental setup, hallucination categories that had higher inter-annotator agreement tended to have higher optimal decision thresholds whereas categories with lower agreement tended to have lower optimal decision thresholds. Our parameter-efficient method achieved ranks 18th, 16th, 16th, and 15th place on the English, French, Italian, and Chinese tracks, respectively, showing promising performance for multilingual fine-grained hallucination detection.