@inproceedings{lin-etal-2026-decoding,
title = "Decoding Time Series with {LLM}s: A Multi-Agent Framework for Cross-Domain Annotation",
author = "Lin, Minhua and
Chen, Zhengzhang and
Liu, Yanchi and
Zhao, Xujiang and
Wu, Zongyu and
Wang, Junxiang and
Zhang, Xiang and
Wang, Suhang and
Chen, Haifeng",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.329/",
pages = "6244--6281",
ISBN = "979-8-89176-386-9",
abstract = "Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods."
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<abstract>Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.</abstract>
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%0 Conference Proceedings
%T Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
%A Lin, Minhua
%A Chen, Zhengzhang
%A Liu, Yanchi
%A Zhao, Xujiang
%A Wu, Zongyu
%A Wang, Junxiang
%A Zhang, Xiang
%A Wang, Suhang
%A Chen, Haifeng
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F lin-etal-2026-decoding
%X Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.
%U https://aclanthology.org/2026.findings-eacl.329/
%P 6244-6281
Markdown (Informal)
[Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation](https://aclanthology.org/2026.findings-eacl.329/) (Lin et al., Findings 2026)
ACL
- Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, and Haifeng Chen. 2026. Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation. In Findings of the Association for Computational Linguistics: EACL 2026, pages 6244–6281, Rabat, Morocco. Association for Computational Linguistics.