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Xi'an Jiaotong University
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QUMem revolutionizes user-state inference by enabling LLMs to retrieve contextually relevant memories that evolve over time, outperforming existing methods on key benchmarks.
ST-Omni-R1 not only excels in sound-event recognition but also sets a new standard for spatial audio reasoning, achieving nearly double the accuracy of existing models.
Even the best multimodal models struggle to understand dynamic charts, achieving only 84.5% accuracy on the new ChartAct benchmark that requires interaction to reveal key information.
Smaller LLMs can achieve superior optimization performance by inheriting structured knowledge distilled from the memories of larger models, without any training.
