Injecting Context via Situation Working Memory for Logical Reasoning with LLMs

ACL ARR 2026 January Submission9569 Authors

Keywords: logical reasoning, working memory

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 \textit{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.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Question Answering;Machine Learning for NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 9569