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