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Fudan University
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Even the top-performing language models struggle with archive-grounded reasoning, achieving only 59.4% accuracy on a benchmark designed to test their agentic capabilities across diverse workplace documents.
MATE achieves a 4.75% boost in robotic manipulation success rates under data scarcity by leveraging specialized expert routing across heterogeneous inputs.
