@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Map-of-Actions: Deliberate Reasoning over Multi-Labeled Graphs
%A Ni, Wuguang
%A Yang, Kai
%Y Gupta, Vivek
%Y Ding, Kaize
%Y Kokel, Harsha
%Y Zhao, Yue
%Y Agarwal, Amit
%Y Wang, Yu
%Y Glass, Michael
%Y Zhang, Yu
%Y Srinivas, Kavitha
%Y Chen, Xiusi
%Y Hassanzadeh, Oktie
%Y Zhu, Qi
%Y Chang, Shuaichen
%Y Luo, Yuan
%S Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-406-4
%F ni-yang-2026-map
%X 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.
%U https://aclanthology.org/2026.surgellm-1.15/
%P 241-259
Markdown (Informal)
[Map-of-Actions: Deliberate Reasoning over Multi-Labeled Graphs](https://aclanthology.org/2026.surgellm-1.15/) (Ni & Yang, SURGeLLM 2026)
ACL
- Wuguang Ni and Kai Yang. 2026. Map-of-Actions: Deliberate Reasoning over Multi-Labeled Graphs. In Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026), pages 241–259, San Diego, California, United States. Association for Computational Linguistics.