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A model-agnostic, tool-augmented framework that grounds frozen VLMs using seven specialized dental vision experts and releases the framework, all seven expert models, and a dentist-annotated dataset for alveolar bone-loss detection in panoramic radiographs.
A new code-mixed dataset reveals that existing models struggle to detect sarcasm, offense, and vulgarity in Bangla-English text, despite performing well on humor detection.
Finally, a reliable, reference-free metric exists to evaluate factual consistency in Bangla summarization, unlocking progress in this under-resourced language.
Despite the trend of larger models leading in medical QA, Llama-4-Maverick-17B's competitive performance hints at valuable efficiency trade-offs for real-world deployment.