Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation

Abstract

Reasoning in complex, poorly understood domains requires methods that tolerate incompleteness while still preserving progress. Large Language Models (LLMs) are effective exploratory tools, but their stateless and non-authoritative nature leads to repetitive rediscovery and loss of hard-won understanding. This paper introduces Cognitive Memoisation as a lightweight knowledge-engineering pattern that externalises facts, constraints, and invariants into human-governed artefacts. When paired with LLMs, Cognitive Memoisation enables sustained exploratory modelling, prevents Groundhog Day–style rediscovery, and provides a disciplined bridge from early abstraction to later formalisation.