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MLLMs can aggressively prune visual tokens without sacrificing performance by adapting token reduction strategies to specific classes and prompts.
LLM-derived abstractions can significantly boost analogical reasoning in narratives, outperforming end-to-end LLMs and offering a more interpretable, modular approach.
