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This work proposes a new paradigm for open-world multimodal perception that integrates semantic depth and adaptability, and proposes a meta-policy gradient-based adaptive controller, which dynamically adjusts feature fusion weights, propagation depth, and attention topology based on the detection state and historical trajectories.