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Shanghai Jiao Tong University
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LLMs, when combined with efficient indexing, can extract actionable incidents from just a handful of noisy user descriptions in real-time, enabling rapid anomaly detection in large-scale cloud services.
MLLMs can now efficiently process 10K-frame videos without training, by adaptively selecting tokens based on the model's own uncertainty about the content.
Forget scaling model size – QuitoBench reveals that simply scaling training data delivers bigger gains for time series forecasting, across both deep learning and foundation models.
