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This paper introduces grounded glossary generation for Classical Sanskrit, a task involving recovering Sanskrit phrases and producing translation-grounded meanings from sloka-translation pairs. It constructs a benchmark from Hindu texts and evaluates various AI models, finding that instruction fine-tuning improves performance, with morphological modeling identified as a key challenge.