@inproceedings{ni-yang-2026-map,
    title = "Map-of-Actions: Deliberate Reasoning over Multi-Labeled Graphs",
    author = "Ni, Wuguang  and
      Yang, Kai",
    editor = "Gupta, Vivek  and
      Ding, Kaize  and
      Kokel, Harsha  and
      Zhao, Yue  and
      Agarwal, Amit  and
      Wang, Yu  and
      Glass, Michael  and
      Zhang, Yu  and
      Srinivas, Kavitha  and
      Chen, Xiusi  and
      Hassanzadeh, Oktie  and
      Zhu, Qi  and
      Chang, Shuaichen  and
      Luo, Yuan",
    booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.surgellm-1.15/",
    pages = "241--259",
    ISBN = "979-8-89176-406-4",
    abstract = "Multi-step reasoning in large language models (LLMs) is typically expressed as unstructured text, making intermediate states difficult to organize, verify, and revise explicitly. This limitation often leads to redundant reasoning paths, error accumulation, and limited controllability in complex tasks. We propose Map-of-Actions (MoA), a neuro-symbolic reasoning framework that treats reasoning as operations over an explicit structured state space. MoA represents intermediate states as a multi-labeled graph, in which each node corresponds to a semantically labeled reasoning unit. This representation provides LLMs with structured memory, explicit state transitions, and flexible interfaces to external tools. Experiments on multiple complex question answering (QA) benchmarks show that MoA consistently outperforms strong baselines, improving accuracy by up to 17.9 percentage points."
}