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This paper proposes NaviGen, a framework for personalized multimodal content generation that encodes user behavior into executable instructions using a dual identifier and a two-stage SFT+RL pipeline, improving personalization across product, game, and short-video domains.
Latte introduces a framework that represents personalization as forecasting a peer-anchored relative preference state using latent trajectories, injecting a soft token into a frozen LLM to achieve personalized generation. It outperforms existing personalization methods on Amazon Reviews 2023 and MemoryCD datasets.