双视觉表示提升时序推理：Qwen3-VL-8B 上 TSRBench 准确率从 44.5% 升至 53.2%
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
一篇论文让 Qwen3-VL-8B 同时看折线图和延迟嵌入，时序推理准确率从 44.5% 拉到 53.2%，做时序分析的可以看看这个双视图思路。
论文提出将互补表示作为时序语言模型的设计原则：折线图保留绝对测量值与坐标框架，归一化延迟嵌入则消除幅度缩放和偏移、凸显时序结构。模型基于 Qwen3-VL-8B，用两个视觉编码器通过视觉 token 和多级特征融合将两种视图接入同一个语言模型。在 TSRBench 上，双视图将整体准确率从单折线图的 44.5% 提到 53.2%，超过最强专用 TSLM 基线的 36.7%；TSExam 达 89.9%，多变量 ChatTS 对齐的数值准确率为 86.8%。消融实验显示两种视图具有不同的不变性特征，组合带来的收益主要体现在预测和推理任务。
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-series representations preserve, and what variation should they factor out? Existing TSLMs typically expose the language model to a single representation, imposing a fixed inductive bias across downstream tasks. We instead introduce complementary representations as a design principle for time-series reasoning. Our model combines two visual views: line plots preserve absolute measurements and the observed coordinate frame, while normalized delay embeddings factor out positive amplitude rescaling and offsets while exposing temporal structure. Dedicated visual encoders integrate both views into a shared language model through visual tokens and multi-level feature fusion. Evaluation-time interventions verify that the two views exhibit distinct invariance profiles, while single-view ablations show that their combination is particularly beneficial beyond basic perception, with the large gains in prediction and reasoning. Built on Qwen3-VL-8B, our dual-view model improves overall TSRBench accuracy from 44.5% with line plots alone to 53.2%, substantially exceeding the strongest evaluated dedicated TSLM baseline at 36.7%. It further achieves 89.9% accuracy on TSExam and 86.8% numerical accuracy on multivariate ChatTS alignment. Together, these results demonstrate that complementary representations provide an effective design principle for general-purpose TSLMs, enabling models to exploit temporal structure without sacrificing access to the original measurement frame.
