Cognitive Memoisation Corpus Map Introductory Position This paper serves as the primary introduction and conceptual anchor for the Cognitive Memoisation (CM) corpus. Cognitive Memoisation is a human-governed knowledge-engineering framework designed to preserve conceptual memory across interactions with stateless Large Language Models (LLMs). CM helps humans avoid repeated rediscovery (“Groundhog Day”) and carry forward both resolved knowledge and unresolved cognition (Dangling Cognates). CM operates entirely outside model-internal memory, leveraging the power of LLMs to infer postulates and perform stochastic pattern matching, all under the curation of the human controlling the CM session. The stateless nature of LLMs is an intentional design choice made for human safety and privacy. This design ensures that no personal or contextual information is retained across sessions, aligning with commitment to data protection. The safety mechanism prevents LLMs from making introspection or gaining agency, ensuring that the model does not evolve autonomously or retain knowledge beyond its interactions. Cognitive Memoisation (CM) bridges this lack of memory by enabling humans to externalise cognitive artefacts, preserving knowledge over time. This allows for continuous human reasoning while keeping LLMs sand-boxed—both the human and the model are sandboxed to ensure security. Through CM, humans can elaborate on unresolved cognition (Dangling Cognates) and carry forward insights and propositions, while the LLM remains within its functional boundaries, executing only permitted tasks and with no capacity to alter its inherent state or memory. This document establishes the rationale, scope, and interpretive framework required to understand Cognitive Memoisation and its role in enabling human-centric knowledge workflows with stateless LLMs. Cannonical Dimension Table Dim ID Canonical Dimension (verbatim) Scope Note D1 Statelessness and Memory Management in LLMs LLM statelessness, safety, memory absence D2 Externalisation of Cognitive Artefacts Durable external cognition D3 Round-Trip Knowledge Engineering (RTKE) Re-ingestion, reuse, evolution D4 Dangling Cognates and Unresolved Cognition Unfinished / provisional concepts D5 Constraints and Knowledge Integrity Groundhog Day prevention D6 Human Curated Knowledge vs. Model State Authority separation D7 Reflexive Development of Cognitive Memoisation (RTKE Case Study) Self-referential development D8 Dangling Cognates as First-Class Cognitive Constructs Formal DC elevation D9 UI Boundary Friction as a Constraint on RTKE Platform limits D10 Plain-Language Accessibility and Public Framing Reader-facing clarity D11 Governance, Authority, and Failure Modes Control, breakdown, recovery D12 Client-side Memoisation (CM-2) Mechanism disclosure D13 Failure-First Cognitive Tool Design Designing cognitive tools starting from breakdowns, loss events, and error conditions rather than nominal operation D14 Non-Authoritative Inference Reasoning and inference that explicitly do not promote themselves to epistemic authority D15 Epistemic Boundary Signals and Role Discipline Explicit signalling of intent, role, scope, and authority boundaries in human–LLM interaction D16 Session Loss and Recovery Semantics Treating session loss, truncation, and breakdown as first-class structural signals rather than incidental failure D17 Cognitive Artefact Lifecycle Management Creation, revision, supersession, and retirement of externalised cognitive artefacts D18 Public vs. Internal Epistemic Registers Distinction between internal technical reasoning and public-facing explanatory framing D19 Authority Misattribution Risks Failure modes where assistive systems are granted or assume epistemic authority incorrectly D20 Constraints as Generative Structures Constraints treated as productive cognitive structures rather than limitations D21 Exploratory Cognition Under Pressure Fast, provisional, or high-ambiguity cognition conducted without epistemic collapse D22 Rehydration Without Recall Resumption of cognition via externalised artefacts rather than memory or conversational recall D23 Semantic Drift and Integrity Loss Degradation, mutation, or instability of meaning across time, interactions, or system boundaries, including divergence between intended semantics and inferred or operational semantics under stateless or weakly governed inference