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Current LLM agents still struggle to infer and leverage user preferences from fragmented, real-world interactions, revealing a substantial gap between their capabilities and the demands of personalized decision-making.
LLMs and LVLMs share more than half their top-activated neurons during multi-step inference, opening a surprisingly cheap path to boost vision-language reasoning by transplanting skills from text-only models.
