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The paper proposes the Plan, Learn, Adapt (PLA) framework for personalized on-device itinerary generation, combining feasibility-guaranteed combinatorial planning with human preference learning via a Bradley-Terry reward model. In deployment, it achieved a 91% increase in itinerary completion rates with low latency, outperforming frontier LLMs in feasibility.