Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering

• URL: http://arxiv.org/abs/2606.18986v1
• Date: Wed, 17 Jun 2026 12:07:23 GMT
• Title: Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
• Abstract summary: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA)<n>TSQA formulates time-series analysis as natural-language question answering.<n>To address this challenge, we propose CADE, a novel framework for TSQA built upon two key components: direct timestep embedding and semantic alignment.
• Score: 13.074536659496362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable tokens whose embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, locking in one granularity that breaks patterns and hides exact timesteps, through a separate module that rarely transfers across datasets with different lengths or sampling rates. To address this challenge, we propose CADE (Contrastive Alignment with Direct Embedding), a novel framework for TSQA built upon two key components: direct timestep embedding and semantic alignment. The proposed framework maps each timestep directly into the LLM embedding space through a point-wise linear encoder and MLP projector, preserving exact index-level access while eliminating the need for patching and padding. To further bridge the semantic gap between time-series and language representations, we introduce a novel one-directional supervised contrastive loss that aligns time-series embeddings with frozen class-name text anchors. Experimental results on the public Time-MQA benchmark demonstrate that our framework consistently improves performance across six TSQA tasks, outperforming both open-source and proprietary LLM baselines.

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