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Delays in cloud-side inference can be effectively compensated, retaining over 80% performance in imitation-learning tasks even under significant latency.
Task-conditional faithfulness failures in multimodal LLMs can be detected and corrected, ensuring models use appropriate evidence for grid diagnosis.
