CADE framework proposes direct timestep embedding and contrastive alignment for time-series question answering
A new arXiv preprint introduces CADE (Contrastive Alignment with Direct Embedding), a framework for time-series question answering (TSQA) that bypasses the tokenization bottleneck of standard LLMs by mapping each timestep directly into the LLM embedding space via a point-wise linear encoder and MLP projector. The approach also introduces a one-directional supervised contrastive loss to align time-series embeddings with frozen class-name text anchors, bridging the semantic gap between numerical and language representations. Evaluated on the Time-MQA benchmark across six TSQA tasks, CADE outperforms both open-source and proprietary LLM baselines. The work addresses a concrete limitation of patch-based encoders — fixed granularity and poor cross-dataset transfer — with a cleaner architectural alternative.
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