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Injecting Context via Situation Working Memory for Logical Reasoning with LLMs
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Injecting Context via Situation Working Memory for Logical Reasoning with LLMs
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.
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