@inproceedings{ni-etal-2026-streasoner,
    title = "{STR}easoner: Empowering {LLM}s for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning",
    author = "Ni, Juntong  and
      Wang, Shiyu  and
      He, Qi  and
      Jin, Ming  and
      Jin, Wei",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.702/",
    pages = "15369--15416",
    ISBN = "979-8-89176-390-6",
    abstract = "Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capability is vital for high-stakes decision-making in systems such as traffic networks, power grids, and disease propagation. However, the field remains underdeveloped because most existing works prioritize predictive accuracy over reasoning. To address the gap, we introduce ST-Bench, a benchmark consisting of four core tasks, including etiological reasoning, entity identification, correlation reasoning, and in-context forecasting, developed via a network SDE-based multi-agent data synthesis pipeline. We then propose STReasoner, a model that integrates time series, graph structure, and text for explicit reasoning. To promote spatially grounded logic, we introduce S-GRPO, a reinforcement learning algorithm that rewards performance gains specifically attributable to spatial information. Experiments show that STReasoner achieves average accuracy gains between 17{\%} and 135{\%} at only 0.004x the cost of proprietary models and generalizes robustly to real-world data."
}