让大模型学会像人一样推理时间序列，靠的是引导性的逻辑链条。
Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
- 用标签条件生成从观测到结果的推理路径作为引导
- 混合检索机制兼顾时间模式与语义上下文，精准匹配先验逻辑
- 在三个领域任务中显著提升推理能力，适合需要可解释性的时间分析
现有多模态大模型在时间序列推理中表现不佳，源于缺乏将时序观测与下游结果关联的推理先验，导致模型依赖表面模式匹配而非严谨推理。为此，我们提出基于推理依据的上下文学习方法，将推理过程作为引导而非事后解释，并构建RationaleTS方法。首先，生成由可观测证据通向潜在结果的标签条件推理路径；其次，设计混合检索机制，平衡时间模式与语义上下文，为新样本的上下文推理检索相关推理先验。在三个领域的时序推理任务上开展广泛实验，验证了RationaleTS的有效性与高效性。代码将公开以供复现。
原文摘要 · Abstract (English)
The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their downstream outcomes, which leads models to rely on superficial pattern matching rather than principled reasoning. We therefore propose the rationale-grounded in-context learning for time series reasoning, where rationales work as guiding reasoning units rather than post-hoc explanations, and develop the RationaleTS method. Specifically, we firstly induce label-conditioned rationales, composed of reasoning paths from observable evidence to the potential outcomes. Then, we design the hybrid retrieval by balancing temporal patterns and semantic contexts to retrieve correlated rationale priors for the final in-context inference on new samples. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed RationaleTS on three-domain time series reasoning tasks. We will release our code for reproduction.
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