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Xidian University Xi'an
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Ride-hailing dispatchers do not need brittle reward engineering: training a preference reward model directly on implicit multi-view passenger and driver trajectories outperforms heuristic-tuned hold policies in live, city-scale deployment.
Reasoning segmentation models stumble by cramming semantic interpretation and spatial localization into a single prompt token; decoupling "what" from "where" hits state-of-the-art grounding while fine-tuning less than 0.4% of the base MLLM.
