@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://preview.aclanthology.org/ingest-eacl/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."
}