@inproceedings{liu-etal-2026-rationale,
title = "Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models",
author = "Liu, Qingxiang and
Cui, Zhiqing and
Luo, Xiaoliang and
Wu, Yuqian and
Jiang, Zhuoyang and
Wan, Huaiyu and
Sun, Sheng and
Wang, Lvchun and
Yu, Wei and
Liang, Yuxuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1816/",
pages = "38806--38824",
ISBN = "979-8-89176-462-0",
abstract = "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 provide the source code in https://github.com/yuppielqx/RationaleTS."
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<abstract>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 provide the source code in https://github.com/yuppielqx/RationaleTS.</abstract>
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%0 Conference Proceedings
%T Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
%A Liu, Qingxiang
%A Cui, Zhiqing
%A Luo, Xiaoliang
%A Wu, Yuqian
%A Jiang, Zhuoyang
%A Wan, Huaiyu
%A Sun, Sheng
%A Wang, Lvchun
%A Yu, Wei
%A Liang, Yuxuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-rationale
%X 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 provide the source code in https://github.com/yuppielqx/RationaleTS.
%U https://aclanthology.org/2026.findings-emnlp.1816/
%P 38806-38824
Markdown (Informal)
[Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models](https://aclanthology.org/2026.findings-emnlp.1816/) (Liu et al., Findings 2026)
ACL
- Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo, Yuqian Wu, Zhuoyang Jiang, Huaiyu Wan, Sheng Sun, Lvchun Wang, Wei Yu, and Yuxuan Liang. 2026. Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 38806–38824, Budapest, Hungary. Association for Computational Linguistics.