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        <title>Injecting Context via Situation Working Memory for Logical Reasoning with LLMs</title>
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    <abstract>Recent advances in large language models (LLMs) have improved logical reasoning by injecting formal logic or explicit structured representations. However, such methods often lose track of what is true now in multi-step reasoning, failing to maintain a coherent global state and its logical consequences. Motivated by Situation Model Theory in cognitive psychology, which views comprehension as constructing and updating a mental model of events along key dimensions (time, space, causality, intention, protagonist), we propose Situation Working Memory (SituW), a cognitively inspired method for contextual reasoning in LLMs. SituW first builds a situation representation by decomposing text along these five dimensions, then guides LLM inference with this evolving state. Keeping an explicit, dynamically updated situation memory instead of a static logical form encourages globally consistent reasoning over the situation model rather than raw text. Evaluated in both supervised and unsupervised settings, SituW improves accuracy by 23.3%p and 15.93%p while reducing “uncertain” predictions, suggesting that explicit situation modeling supports more globally consistent LLM reasoning.</abstract>
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%0 Conference Proceedings
%T Injecting Context via Situation Working Memory for Logical Reasoning with LLMs
%A Kim, Jieun
%A Lim, Seoha
%A Choi, YoungHae
%A Cho, Sung-Bae
%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 kim-etal-2026-injecting
%X Recent advances in large language models (LLMs) have improved logical reasoning by injecting formal logic or explicit structured representations. However, such methods often lose track of what is true now in multi-step reasoning, failing to maintain a coherent global state and its logical consequences. Motivated by Situation Model Theory in cognitive psychology, which views comprehension as constructing and updating a mental model of events along key dimensions (time, space, causality, intention, protagonist), we propose Situation Working Memory (SituW), a cognitively inspired method for contextual reasoning in LLMs. SituW first builds a situation representation by decomposing text along these five dimensions, then guides LLM inference with this evolving state. Keeping an explicit, dynamically updated situation memory instead of a static logical form encourages globally consistent reasoning over the situation model rather than raw text. Evaluated in both supervised and unsupervised settings, SituW improves accuracy by 23.3%p and 15.93%p while reducing “uncertain” predictions, suggesting that explicit situation modeling supports more globally consistent LLM reasoning.
%U https://aclanthology.org/2026.acl-long.1499/
%P 32467-32480

Markdown (Informal)

[Injecting Context via Situation Working Memory for Logical Reasoning with LLMs](https://aclanthology.org/2026.acl-long.1499/) (Kim et al., ACL 2026)

• Injecting Context via Situation Working Memory for Logical Reasoning with LLMs (Kim et al., ACL 2026)

ACL

• Jieun Kim, Seoha Lim, YoungHae Choi, and Sung-Bae Cho. 2026. Injecting Context via Situation Working Memory for Logical Reasoning with LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 32467–32480, San Diego, California, United States. Association for Computational Linguistics.