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arXiv · 2610.33410

Benchmarking System One Models for Time-Series Decision Making: A Case Study of Jev

Jianye Wang·Kexin Zhang·Jiayin Xiong·Hao Xue
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Abstract

Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly output answers and their probabilities, which we call Structured System 1 models. Their applicability to time-series tasks remains underexplored. Using Jev 1.13.0 as a case study, we evaluate time-series classification, event type prediction, oil temperature direction and stock movement prediction, converting continuous forecasting targets into discrete decisions to fit the model interface. We establish a unified evaluation framework using accuracy and AUC. Results show stronger performance on selected explicit temporal patterns and event type prediction, but near-chance discrimination on many numerical sequence classification tasks and stock movement prediction. Some datasets also reveal a gap between ranking quality and final decision accuracy. These findings motivate improvements in temporal representation, domain adaptation, and decision-score alignment. Standardized sample construction, model calls, and result recording support extending the evaluation to future models.

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Jianye Wang, Kexin Zhang, Jiayin Xiong, Hao Xue. 2026-10-24. Benchmarking System One Models for Time-Series Decision Making: A Case Study of Jev. https://arxiv.org/abs/2610.33410

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