@inproceedings{tsang-etal-2026-autograph,
    title = "{A}uto{G}raph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction",
    author = "Tsang, Hong Ting  and
      Bai, Jiaxin  and
      Huang, Haoyu  and
      Xiao, Qiao  and
      Zheng, Tianshi  and
      Xu, Baixuan  and
      Liu, Shujie  and
      Song, Yangqiu",
    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.1070/",
    pages = "23351--23374",
    ISBN = "979-8-89176-390-6",
    abstract = "Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, we introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). AutoGraph-R1 trains an LLM constructor by framing graph generation as a policy learning problem, where the reward is derived from the graph{'}s functional utility in a RAG pipeline. We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices. Across multiple QA benchmarks, AutoGraph-R1 consistently enables graph RAG methods to achieve significant performance gains over using task-agnostic baseline graphs. Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically `good{`} graphs to building demonstrably `useful{`} ones."
}