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Kuafu's ability to synthesize robust policies from language and task semantics achieves over 82% success in complex manipulation tasks, revolutionizing how we approach control-to-learning paradigms.
Intermediate embeddings in distributed MLLM frameworks can leak sensitive image prompts, with new attacks achieving 100% extraction accuracy and revealing serious privacy implications.
