@inproceedings{ying-etal-2026-multi,
title = "Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement",
author = "Ying, Wangyang and
Liu, Yanchi and
Zhao, Xujiang and
Cheng, Wei and
Chen, Zhengzhang and
Yu, Wenchao and
Fu, Yanjie and
Chen, Haifeng",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.158/",
pages = "3021--3034",
ISBN = "979-8-89176-386-9",
abstract = "Automatically extracting workflows as procedural graphs from natural language is a promising yet underexplored task that requires ensuring both structural validity and logical alignment. Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. We introduce , a multi-agent framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. The framework operates in three iterative stages: (1) an LLM-based graph extraction phase, (2) a structural feedback phase where a simulation agent diagnoses and explains structural issues, and (3) a logical feedback phase where a semantic agent aligns semantics between flow logic and linguistic cues in the source text. Important feedback is prioritized and expressed in natural language, which is injected into the next-round prompt, enabling interpretable and controllable refinement. This modular design allows agents to target distinct error types without supervision or parameter updates. Experiments demonstrate that achieves substantial improvements in both structural correctness and logical consistency over strong baselines."
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<abstract>Automatically extracting workflows as procedural graphs from natural language is a promising yet underexplored task that requires ensuring both structural validity and logical alignment. Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. We introduce , a multi-agent framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. The framework operates in three iterative stages: (1) an LLM-based graph extraction phase, (2) a structural feedback phase where a simulation agent diagnoses and explains structural issues, and (3) a logical feedback phase where a semantic agent aligns semantics between flow logic and linguistic cues in the source text. Important feedback is prioritized and expressed in natural language, which is injected into the next-round prompt, enabling interpretable and controllable refinement. This modular design allows agents to target distinct error types without supervision or parameter updates. Experiments demonstrate that achieves substantial improvements in both structural correctness and logical consistency over strong baselines.</abstract>
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%0 Conference Proceedings
%T Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement
%A Ying, Wangyang
%A Liu, Yanchi
%A Zhao, Xujiang
%A Cheng, Wei
%A Chen, Zhengzhang
%A Yu, Wenchao
%A Fu, Yanjie
%A Chen, Haifeng
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F ying-etal-2026-multi
%X Automatically extracting workflows as procedural graphs from natural language is a promising yet underexplored task that requires ensuring both structural validity and logical alignment. Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. We introduce , a multi-agent framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. The framework operates in three iterative stages: (1) an LLM-based graph extraction phase, (2) a structural feedback phase where a simulation agent diagnoses and explains structural issues, and (3) a logical feedback phase where a semantic agent aligns semantics between flow logic and linguistic cues in the source text. Important feedback is prioritized and expressed in natural language, which is injected into the next-round prompt, enabling interpretable and controllable refinement. This modular design allows agents to target distinct error types without supervision or parameter updates. Experiments demonstrate that achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
%U https://aclanthology.org/2026.findings-eacl.158/
%P 3021-3034
Markdown (Informal)
[Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement](https://aclanthology.org/2026.findings-eacl.158/) (Ying et al., Findings 2026)
ACL
- Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, and Haifeng Chen. 2026. Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3021–3034, Rabat, Morocco. Association for Computational Linguistics.