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.

Political memes have become an effective tool for online political communication and engagement. Their interpretation is particularly challenging in low-resource and code-mixed languages such as Tamil, where memes often include Tamil-English, use culturally relevant lexicon, incorporate political entities, and utilize references to prominent personalities and parties. This paper presents our submission to the MMLow shared task at AACL-IJCNLP 2026 on multi-level classification of Tamil political memes, which requires jointly predicting (i) a coarse-grained stance label (Support vs. Troll) and (ii) a fine-grained target label identifying whether the meme concerns a political party or a person. We proposed a lightweight hierarchical classifier built on frozen, pretrained multimodal encoders: images are represented using CLIP (ViT-B/32) and text using the multilingual BGE-M3 embedding model, with the two modalities combined via early, non-parametric concatenation fusion. A shared encoder feeds two prediction heads, where the target-identification head is explicitly conditioned on the stance head’s output logits via a gradient-detachment mechanism, allowing the model to respect the task’s label hierarchy without corrupting the coarse-level classifier’s gradients. To address pronounced class imbalance in both label levels, we applied balanced loss weighting at each level. On the test set of unlabeled data, our system achieved 3rd place, with macro-F1 scores of 0.856 for stance classification and 0.532 for target identification, yielding a final score of 0.694, indicating promising performance on the Tamil political meme classification problem.

Political memes communicate opinions using the interplay between visual and textual information, posing a challenging multimodal classification task. We present a hierarchical multimodal framework for political meme classification, which decomposes the task into stance and target entity prediction. Our framework was evaluated on the data from the Multi-Level Political Meme Classification Shared Task at MMLow @ AACL-IJCNLP 2026. The task comprised two levels of classification: (i) predicting the political stance of a meme as either Support/Praise or Troll/Oppose, and (ii) identifying the target of the meme combined with the stance. We approached the task by first predicting the Level-1 labels followed by predicting the target entity (Person/Party) and then combining them in a deterministic manner to predict the final Level-2 labels. Our method used a SigLIP vision transformer for stance classification and a hybrid SigLIP-XLM-RoBERTa architecture for target classification with late fusion. We trained our models using 5-fold stratified cross-validation with checkpoint ensembling. We also performed ablation experiments to analyze the contribution of individual modalities, observing that visual features are more informative for stance prediction while textual features contain more information for target identification, which supports the multimodal task at hand. Our system achieved a macro F1 score of 0.9027 and 0.6694 for Level-1 and Level-2 classification, respectively, achieving a final score of 0.7860, ranking first in the shared task.