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Institute of Software, University of Chinese Academy of Sciences, Chinese Academy of Sciences
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Over-smoothing in time series forecasts can be mitigated by a novel framework that preserves distinct dynamic modes, leading to more accurate and diverse predictions.
LLMs' vulnerability to adversarial prefixes isn't just about lacking safety training data, but a deeper problem of "semantic representation decay" that a causal approach can fix.
