Injecting Context via Situation Working Memory for Logical Reasoning with LLMs

Recent advances in large language models (LLMs) have improved logical reasoning by incorporating formal logic or explicit structured representations. However, such methods often lose track of what is true now in multistep 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 a cognitively inspired method of Situation Working Memory (SituW) for contextual reasoning in LLMs. SituW first builds a situation representation by decomposing text along these five dimensions, and guides LLM inference with the 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 prompt-based 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.