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Proactive anomaly anticipation in contact-rich manipulation is achieved by predicting future physical interactions, enabling earlier detection and improved task success.
SemPOI-RL not only boosts the quality of out-of-town POI recommendations but also makes the underlying reasoning interpretable, bridging the gap between LLM capabilities and structured generation.