@inproceedings{huang-etal-2026-temptool,
    title = "{T}emp{T}ool-R1: Tool-Augmented Reinforcement Learning for Temporal Knowledge Graph Question Answering",
    author = "Huang, Zicheng  and
      Tong, Yajuan  and
      Tu, Xinhui  and
      He, Tingting",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.797/",
    pages = "16224--16241",
    ISBN = "979-8-89176-395-1",
    abstract = "Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries over temporal knowledge graphs, but existing approaches struggle with multi-hop reasoning and implicit temporal constraints. We introduce TempTool-R1, a novel tool-integrated reasoning framework that enables large language models to explicitly use temporal tools for precise reasoning. First, we design a unified temporal tool-based API capable of transforming implicit temporal cues into executable operations, establishing the structural foundation for tool interaction. In the second stage, supervised fine-tuning teaches the model to interweave chain-of-thought reasoning with think-then-tool usage, allowing it to call temporal tools during inference. Finally, we apply reinforcement learning with fine-grained, order-sensitive reward functions tailored for temporal tool use, further refining the model{'}s tool-use policy. Experiments on three challenging TKGQA benchmarks demonstrate that TempTool-R1 significantly outperforms existing methods. In particular, our approach excels on complex questions requiring multi-hop temporal reasoning, highlighting the effectiveness of temporal tool integration and reward optimization in improving TKGQA performance."
}