CADE introduces direct timestep embedding and contrastive alignment to preserve metric structure in time-series data. By mapping each timestep directly into LLM embedding space, it avoids tokenization bottlenecks and outperforms existing LLM baselines on six TSQA tasks.
CADE: Direct Timestep Embedding for Time-Series Question Answering
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