@inproceedings{gong-etal-2026-temp,
    title = "Temp-R1: A Unified Autonomous Agent for Complex Temporal {KGQA} via Reverse Curriculum Reinforcement Learning",
    author = "Gong, Zhaoyan  and
      Liu, Zhiqiang  and
      Li, Songze  and
      Guo, Xiaoke  and
      Liu, Yuanxiang  and
      Deng, Xinle  and
      Liu, Zhizhen  and
      Liang, Lei  and
      Chen, Huajun  and
      Zhang, Wen",
    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.1184/",
    pages = "25826--25845",
    ISBN = "979-8-89176-390-6",
    abstract = "Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex temporal constraints. Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. We propose **Temp-R1**, the first autonomous end-to-end agent for TKGQA trained through reinforcement learning. To address cognitive overload in single-action reasoning, we expand the action space with specialized internal actions alongside external action. To prevent shortcut learning on simple questions, we introduce reverse curriculum learning that trains on difficult questions first, forcing the development of sophisticated reasoning before transferring to easier cases. Our 8B-parameter Temp-R1 achieves state-of-the-art performance on MultiTQ and TimelineKGQA, improving 19.8{\%} over strong baselines on complex questions. Our work establishes a new paradigm for autonomous temporal reasoning agents. The code is available at \url{https://github.com/zjukg/Temp-R1}."
}