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This paper presents the SEAR system for multilingual speech MCQ, achieving 90.92% accuracy on the MLC-SLM Challenge through segment-evidence-aware routing, supervised fine-tuning, and reinforcement learning.
BengaliMCQ is a structure-aware RAG framework using graph neural networks to model hierarchical document structures in Bengali textbooks, enabling automatic generation and answer prediction of academic multiple-choice questions with improved performance over baseline methods.