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This paper presents a systematic benchmark of token pruning—a compression technique that removes tokens and embeddings for irrelevant languages—applied to Korean-centric LLM tasks. The study evaluates popular multilingual models (Qwen3, Gemma-3, Llama-3, Aya) across different vocabulary configurations and finds that token pruning significantly improves generation stability and reduces memory footprint for domain-specific deployments.