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University of Science and Technology of China
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VAKE reveals that over 80% of the knowledge activated through explicit priming is crucial for answering questions, showcasing a new pathway to enhance LLM factual accuracy.
Traditional trajectory-based training collapses policy diversity, but DART-SD's topology-aware approach ensures agents can explore optimal paths without penalty.
UniSAE enables seamless, granular editing of speech attributes, allowing for precise control over speaker, emotion, and content in a unified framework.
