@inproceedings{shin-etal-2026-graphmind,
title = "{G}raph{M}ind: {LLM}s as Dynamic Knowledge Builders for Sequential Decision-Making",
author = "Shin, Sunguk and
Lee, Hayeong and
Seo, Jun Ho and
Lee, Jinho and
Kim, Myunsoo and
Chang, Minsuk and
Lee, Byung-Jun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1651/",
pages = "32985--33007",
ISBN = "979-8-89176-395-1",
abstract = "While the reasoning capabilities of large language models (LLMs) have advanced considerably, efficiently internalizing and leveraging new information in dynamically interactive environments remains a significant challenge. This limitation is particularly pronounced in partially observable environments, which require agents to manage long-term memory and perform effective exploration under incomplete information. To address this, we propose an LLM agent architecture that integrates a knowledge graph as a graph-based memory module. The agent incrementally constructs the knowledge graph through environmental interactions and retrieves relevant information to generate efficient plans. We evaluate our approach in complex navigation tasks specifically designed to present long-horizon and partially observable challenges. Experimental results demonstrate that incorporating a self-extending memory module significantly enhances the performance and efficiency of the LLM{'}s planning capabilities."
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<abstract>While the reasoning capabilities of large language models (LLMs) have advanced considerably, efficiently internalizing and leveraging new information in dynamically interactive environments remains a significant challenge. This limitation is particularly pronounced in partially observable environments, which require agents to manage long-term memory and perform effective exploration under incomplete information. To address this, we propose an LLM agent architecture that integrates a knowledge graph as a graph-based memory module. The agent incrementally constructs the knowledge graph through environmental interactions and retrieves relevant information to generate efficient plans. We evaluate our approach in complex navigation tasks specifically designed to present long-horizon and partially observable challenges. Experimental results demonstrate that incorporating a self-extending memory module significantly enhances the performance and efficiency of the LLM’s planning capabilities.</abstract>
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%0 Conference Proceedings
%T GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making
%A Shin, Sunguk
%A Lee, Hayeong
%A Seo, Jun Ho
%A Lee, Jinho
%A Kim, Myunsoo
%A Chang, Minsuk
%A Lee, Byung-Jun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shin-etal-2026-graphmind
%X While the reasoning capabilities of large language models (LLMs) have advanced considerably, efficiently internalizing and leveraging new information in dynamically interactive environments remains a significant challenge. This limitation is particularly pronounced in partially observable environments, which require agents to manage long-term memory and perform effective exploration under incomplete information. To address this, we propose an LLM agent architecture that integrates a knowledge graph as a graph-based memory module. The agent incrementally constructs the knowledge graph through environmental interactions and retrieves relevant information to generate efficient plans. We evaluate our approach in complex navigation tasks specifically designed to present long-horizon and partially observable challenges. Experimental results demonstrate that incorporating a self-extending memory module significantly enhances the performance and efficiency of the LLM’s planning capabilities.
%U https://aclanthology.org/2026.findings-acl.1651/
%P 32985-33007
Markdown (Informal)
[GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making](https://aclanthology.org/2026.findings-acl.1651/) (Shin et al., Findings 2026)
ACL
- Sunguk Shin, Hayeong Lee, Jun Ho Seo, Jinho Lee, Myunsoo Kim, Minsuk Chang, and Byung-Jun Lee. 2026. GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making. In Findings of the Association for Computational Linguistics: ACL 2026, pages 32985–33007, San Diego, California, United States. Association for Computational Linguistics.