Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language

• URL: http://arxiv.org/abs/2608.05238v1
• Date: Wed, 05 Aug 2026 13:57:08 GMT
• Title: Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
• Abstract summary: Training multimodal models to align time series with language runs into a self-supervision trap.<n>The data can never teach more than the labeler already knows.<n>We resolve this by decoupling perception from description.
• Score: 16.343638992747533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label quality is capped by the perceptual skill the model is supposed to learn. The data can never teach more than the labeler already knows. A second gap makes this worse: most datasets use a single variable, but the patterns that matter (cross-channel correlation, lead-lag structure, co-occurring anomalies) appear only with several variables, right where the labeling LLM's limits are most exposed. These two problems create a trilemma: existing methods are reliable, realistic, or scalable, but none achieves all three. We resolve this by decoupling perception from description. Deterministic code computes a set of statistics from real, open-source multivariate series; the LLM verbalizes those precomputed facts. Perception, which LLMs do poorly, is handled by computation, while the LLM handles expression. This produces CGTime, our 4B-parameter computation-grounded time-series-language model. CGTime outperforms far larger general-purpose models on multivariate understanding tasks: it attains the best multivariate fact score on our held-out benchmark (0.283 vs. 0.173 for GPT-4o-mini and 0.203 for GPT-5.4-nano), a gap that survives Holm-corrected paired significance tests against every baseline. It also states verifiable numerical facts in generated captions more accurately and covers a broader range of statistical properties.

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