Cognitive Memoisation: Governing Knowledge Round-Trip to Prevent Knowledge Erosion in LLM Systems

Scope

This is the Cognitive Memoisation (CM) Purpose Paper.

Anchor

Cognitive Memoisation governs the round-trip of knowledge between humans and stateless AI systems in order to prevent knowledge erosion and preserve human knowledge across sessions and over time.

Abstract

Large Language Model (LLM) systems are inherently stateless. Each interaction is processed within a bounded context that is routinely truncated, paraphrased, or reinterpreted. As a consequence, knowledge introduced during a session erodes through semantic drift, constraint weakening, paraphrasing, or context eviction. Over extended interactions this produces the familiar "Groundhog Day" condition, where previously established facts and constraints must be repeatedly reintroduced in order to maintain progress.

Cognitive Memoisation addresses this problem by externalising knowledge into durable, human-governed artefacts that can be serialised, preserved, and reintroduced into inference as required. Rather than relying on fragile conversational continuity, Cognitive Memoisation establishes a governed round-trip between human cognition, durable knowledge artefacts, and LLM inference. In this model, knowledge is explicitly captured, preserved from erosion, and projected back into reasoning contexts in a controlled and repeatable manner, allowing human knowledge to persist across sessions and over time.

The purpose of Cognitive Memoisation is not to replace human reasoning nor to embed authority within AI systems. Its purpose is to ensure that knowledge produced through human-AI collaboration remains stable, corrigible, and progressively accumulative. By governing the round-trip of knowledge between humans and stateless AI systems, Cognitive Memoisation allows work to compound across sessions without loss of meaning, constraint, or intent.