@inproceedings{chang-etal-2026-enhancing,
title = "Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision",
author = "Chang, Ge and
Su, Jinbo and
Liu, Jiacheng and
Yang, Pengfei and
Shang, Yuhao and
Zheng, Huiwen and
Ma, Hongli and
Liang, Yan and
Li, Yuanchun and
Liu, Yunxin",
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://aclanthology.org/2026.acl-long.1169/",
pages = "25510--25525",
ISBN = "979-8-89176-390-6",
abstract = "Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that fit the LLM context. Existing retrievers often struggle, relying either on shallow embedding similarity or costly interactive policies that require excessive supervision.To address these challenges, we introduce Graph-S$^3$, an agentic textual graph reasoning framework featuring an LLM-based retriever trained with synthetic stepwise supervision. Rather than relying on final answer rewards{---}which often yield sparse and unstable signals{---}we optimize the retriever by evaluating each step against offline-extracted golden subgraphs.Our approach distills golden subgraphs via a specialized data synthesis pipeline to formulate dense rewards, facilitating a two-stage training scheme that effectively learns the interactive graph exploration policy.Based on extensive experiments on three common datasets in comparison with seven strong baselines, our approach achieves an average improvement of 15.6{\%} in accuracy and 17.2{\%} in F$_1$ score. The advantage is even higher in more complicated multi-hop reasoning tasks."
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<abstract>Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that fit the LLM context. Existing retrievers often struggle, relying either on shallow embedding similarity or costly interactive policies that require excessive supervision.To address these challenges, we introduce Graph-S³, an agentic textual graph reasoning framework featuring an LLM-based retriever trained with synthetic stepwise supervision. Rather than relying on final answer rewards—which often yield sparse and unstable signals—we optimize the retriever by evaluating each step against offline-extracted golden subgraphs.Our approach distills golden subgraphs via a specialized data synthesis pipeline to formulate dense rewards, facilitating a two-stage training scheme that effectively learns the interactive graph exploration policy.Based on extensive experiments on three common datasets in comparison with seven strong baselines, our approach achieves an average improvement of 15.6% in accuracy and 17.2% in F₁ score. The advantage is even higher in more complicated multi-hop reasoning tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision
%A Chang, Ge
%A Su, Jinbo
%A Liu, Jiacheng
%A Yang, Pengfei
%A Shang, Yuhao
%A Zheng, Huiwen
%A Ma, Hongli
%A Liang, Yan
%A Li, Yuanchun
%A Liu, Yunxin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chang-etal-2026-enhancing
%X Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that fit the LLM context. Existing retrievers often struggle, relying either on shallow embedding similarity or costly interactive policies that require excessive supervision.To address these challenges, we introduce Graph-S³, an agentic textual graph reasoning framework featuring an LLM-based retriever trained with synthetic stepwise supervision. Rather than relying on final answer rewards—which often yield sparse and unstable signals—we optimize the retriever by evaluating each step against offline-extracted golden subgraphs.Our approach distills golden subgraphs via a specialized data synthesis pipeline to formulate dense rewards, facilitating a two-stage training scheme that effectively learns the interactive graph exploration policy.Based on extensive experiments on three common datasets in comparison with seven strong baselines, our approach achieves an average improvement of 15.6% in accuracy and 17.2% in F₁ score. The advantage is even higher in more complicated multi-hop reasoning tasks.
%U https://aclanthology.org/2026.acl-long.1169/
%P 25510-25525
Markdown (Informal)
[Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision](https://aclanthology.org/2026.acl-long.1169/) (Chang et al., ACL 2026)
ACL
- Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, and Yunxin Liu. 2026. Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25510–25525, San Diego, California, United States. Association for Computational Linguistics.