Revision as of 20:16, 13 January 2026

metadata

Title:	Cognitive Memoisation: corpus guide.
Author:	Ralph B. Holland
version:	2.0.0
Publication Date:	2025-12-22T19:10Z
Update:	2026-01-13T19:09 new dimension table and two projections. 2026-01-06T10:25Z v1.3.0 Includes the release of CM-2 2025-01-04T05:12 v1.1.0 renamed from "Cognitive Memoisation: A framework for human cognition" to "Cognitive Memoisation: corpus guide" Include papers.
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
Provenance:	This is an authored paper maintained as a MediaWiki document as part of the category:Cognitive Memoisation corpus.
Status:	final	=

Metadata (Normative)

The metadata table immediately preceding this section is CM-defined and constitutes the authoritative provenance record for this MWDUMP artefact.

All fields in that table (including artefact, author, version, date, local timezone, and reason) MUST be treated as normative metadata.

The assisting system MUST NOT infer, normalise, reinterpret, duplicate, or rewrite these fields. If any field is missing, unclear, or later superseded, the change MUST be made explicitly by the human and recorded via version update, not inferred.

This document predates its open licensing.
As curator and author, I apply the Apache License, Version 2.0, at publication to permit reuse and implementation while preventing enclosure or patent capture. This licensing action does not revise, reinterpret, or supersede any normative content herein.
Authority remains explicitly human; no implementation, system, or platform may assert epistemic authority by virtue of this license.
(2025-12-18 version 1.0 - See the Main Page)

Cognitive Memoisation: corpus guide.

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 (such as ChatGPT) 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 OpenAI's 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.

Cognitive Memoisation Corpus Map

Canonical Dimension Table (Anchored)

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	ChatGPT 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

Dimension-Centric Projection (Documents Ordered by Time Within Each Dimension)

D1 — Statelessness and Memory Management in LLMs

Time-Ordered Projection with Inline Dimensions

2025-12-17 — FOUNDATION

Progress Without Memory: Cognitive Memoisation as a Knowledge-Engineering Pattern for Stateless LLM Interaction
- D1 — Statelessness and Memory Management in LLMs
- D2 — Externalisation of Cognitive Artefacts
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D5 — Constraints and Knowledge Integrity
- D6 — Human Curated Knowledge vs. Model State

2025-12-18 — COMMUNICATION

Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers)
- D2 — Externalisation of Cognitive Artefacts
- D10 — Plain-Language Accessibility and Public Framing

2025-12-28 — PORTABILITY / SEMANTICS

Dangling Cognates: Preserving Unresolved Knowledge in Cognitive Memoisation
- D4 — Dangling Cognates and Unresolved Cognition
- D8 — Dangling Cognates as First-Class Cognitive Constructs

2026-01-04 — MECHANISM / CORPUS ANCHOR

Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation
- D1 — Statelessness and Memory Management in LLMs
- D2 — Externalisation of Cognitive Artefacts
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D4 — Dangling Cognates and Unresolved Cognition
- D5 — Constraints and Knowledge Integrity
- D6 — Human Curated Knowledge vs. Model State

Cognitive Memoisation: Corpus Guide
- D1 — Statelessness and Memory Management in LLMs
- D2 — Externalisation of Cognitive Artefacts
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D4 — Dangling Cognates and Unresolved Cognition
- D5 — Constraints and Knowledge Integrity
- D6 — Human Curated Knowledge vs. Model State
- D7 — Reflexive Development of Cognitive Memoisation (RTKE Case Study)
- D8 — Dangling Cognates as First-Class Cognitive Constructs
- D9 — ChatGPT UI Boundary Friction as a Constraint on RTKE
- D10 — Plain-Language Accessibility and Public Framing
- D11 — Governance, Authority, and Failure Modes
- D12 — Client-side Memoisation (CM-2)

Journey: Human-Led Convergence in the Articulation of Cognitive Memoisation
- D1 — Statelessness and Memory Management in LLMs
- D2 — Externalisation of Cognitive Artefacts
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D6 — Human Curated Knowledge vs. Model State
- D12 — Client-side Memoisation (CM-2)

2026-01-05 — GOVERNANCE, UI, SYSTEMS

Cognitive Memoisation for Governing Knowledge in Human–AI Collaboration
- D6 — Human Curated Knowledge vs. Model State
- D11 — Governance, Authority, and Failure Modes
- D12 — Client-side Memoisation (CM-2)

ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering
- D1 — Statelessness and Memory Management in LLMs
- D5 — Constraints and Knowledge Integrity
- D9 — ChatGPT UI Boundary Friction as a Constraint on RTKE

Cognitive Memoisation: LLM Systems Requirements for Knowledge Round Trip Engineering
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D5 — Constraints and Knowledge Integrity

Why Cognitive Memoisation Is Not Memorization
- D10 — Plain-Language Accessibility and Public Framing

2026-01-06 — FAILURE & RECOVERY

From UI Failure to Logical Entrapment: A Case Study in Post-Hoc Cognitive Memoisation
- D9 — ChatGPT UI Boundary Friction as a Constraint on RTKE
- D11 — Governance, Authority, and Failure Modes

XDUMP as a Minimal Recovery Mechanism for Round-Trip Knowledge Engineering Under Governance Situated Inference Loss
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D11 — Governance, Authority, and Failure Modes

Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering
- D9 — ChatGPT UI Boundary Friction as a Constraint on RTKE
- D11 — Governance, Authority, and Failure Modes

2026-01-08 — REFLEXIVE & GOVERNANCE THEORY

Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D6 — Human Curated Knowledge vs. Model State
- D7 — Reflexive Development of Cognitive Memoisation (RTKE Case Study)

Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct
- D4 — Dangling Cognates and Unresolved Cognition
- D8 — Dangling Cognates as First-Class Cognitive Constructs

Authority Inversion: A Structural Failure in Human–AI Systems
- D6 — Human Curated Knowledge vs. Model State
- D11 — Governance, Authority, and Failure Modes

2026-01-10 to 2026-01-12 — SYNTHESIS & MYTH-BUSTING

Nothing Is Lost: How to Work with AI Without Losing Your Mind
- D3 — Round-Trip Knowledge Engineering (RTKE)
- D10 — Plain-Language Accessibility and Public Framing

Cognitive Memoisation Is Not Skynet
- D10 — Plain-Language Accessibility and Public Framing

Lopping the Loop with No End in Sight: Circular Reasoning Under Stateless Inference Without Governance
- D1 — Statelessness and Memory Management in LLMs
- D11 — Governance, Authority, and Failure Modes

Cognitive Memoisation: Corpus Mapping and Projection Invariants

Scope and Intent

This artefact enumerates the complete set of invariants required to:

construct the canonical dimension table
assign dimensions to corpus artefacts
produce time-ordered projections
produce divergence (dimension) projections
preserve epistemic discipline, provenance, and human authority

These invariants apply to corpus organisation and projection only. They do not introduce new CM definitions, modify CM-master invariants, or assert governance over reasoning behaviour.

Authority and Epistemic Position

All invariants herein are human-authored and curator-governed.
The assisting system MUST treat this artefact as binding for corpus mapping tasks when asserted.
These invariants govern representation and organisation, not truth, correctness, or inference.

Canonical Dimension Invariants

CM-CORPUS-INV-01 — Dimension Canonicality Invariant

Each dimension MUST have:

a stable identifier (e.g. D1, D2, …)
a single canonical name
a stable semantic scope

Dimension identifiers and names MUST NOT be inferred, renamed, merged, split, or reordered by the assisting system.

CM-CORPUS-INV-02 — Dimension Vocabulary Closure Invariant

The set of dimensions is closed.

No additional dimensions may be introduced unless explicitly declared by the human curator.

Absence of coverage MUST be represented as absence, not as invention.

CM-CORPUS-INV-03 — Dimension Semantic Fidelity Invariant

Assignment of a dimension to an artefact MUST reflect explicit scope alignment present in the artefact itself or in curator-supplied mapping.

The assisting system MUST NOT infer dimension relevance based on stylistic similarity, topic proximity, or semantic guesswork.

Artefact Identification Invariants

CM-CORPUS-INV-04 — Normative Title Fidelity Invariant

Artefacts MUST be referenced using their exact normative MediaWiki page titles.

Paraphrase, abbreviation, or normalisation of titles is prohibited.

CM-CORPUS-INV-05 — Artefact Identity Stability Invariant

An artefact is identified solely by its title and publication date.

Later editorial changes do not create new artefact identities unless explicitly versioned by the human.

Temporal Ordering Invariants

CM-CORPUS-INV-06 — Declared Date Authority Invariant

Time ordering MUST use the declared publication date as supplied by the human curator.

The assisting system MUST NOT infer, estimate, or correct dates.

If multiple dates exist, the curator MUST specify which date governs ordering.

CM-CORPUS-INV-07 — Sequence Over Precision Invariant

Temporal sequence is authoritative even if time precision is coarse.

Relative ordering MUST be preserved even when exact timestamps are unavailable.

Projection Construction Invariants

CM-CORPUS-INV-08 — Projection Non-Inference Invariant

Projections MUST NOT introduce:

new artefacts
new dimensions
new relationships
new interpretations

A projection is a re-expression of existing assignments only.

CM-CORPUS-INV-09 — Projection Completeness Invariant

Within declared scope, projections MUST include all eligible artefacts.

Selective omission constitutes a projection violation.

CM-CORPUS-INV-10 — Multi-Projection Consistency Invariant

All projections MUST be semantically consistent with one another.

Differences between projections may exist only in ordering or grouping, not in content.

Time-Ordered Projection Invariants

CM-CORPUS-INV-11 — Time-Ordered Projection Structure Invariant

A time-ordered projection MUST:

group artefacts by declared date
list artefacts within each group
attach dimensions as subordinate information

Time is the primary axis; dimensions are secondary.

CM-CORPUS-INV-12 — Inline Dimension Expansion Invariant

When dimensions are listed under artefacts:

each dimension MUST include both identifier and full canonical name
users MUST NOT be required to consult a separate table to understand dimension meaning

Divergence (Dimension) Projection Invariants

CM-CORPUS-INV-13 — Dimension-Centric Projection Structure Invariant

A divergence projection MUST:

use dimensions as the primary axis
list all artefacts participating in each dimension
preserve publication dates for temporal context

CM-CORPUS-INV-14 — Non-Exclusivity Invariant

Artefacts MAY appear under multiple dimensions.

Multiplicity is expected and MUST NOT be collapsed.

Representation and Emission Invariants

CM-CORPUS-INV-15 — MediaWiki-Only Emission Invariant

All corpus projections emitted as MWDUMP MUST use MediaWiki syntax exclusively.

Markdown, hybrid markup, or implicit formatting is prohibited.

CM-CORPUS-INV-16 — Bullet Level Semantics Invariant

Bullet depth conveys semantic hierarchy:

one asterisk (*) — artefact
- two asterisks (**) — dimension assignment
  - three asterisks (***) — sub-dimension or note (if present)
    - four asterisks (****) — reserved

The assisting system MUST respect bullet depth semantics.

Human Readability and Governance Invariants

CM-CORPUS-INV-17 — Human Readability Invariant

Corpus projections MUST be intelligible to human readers without external tooling.

Abbreviation without expansion is prohibited.

CM-CORPUS-INV-18 — No Implied Authority Invariant

Presence of an artefact or dimension in a projection MUST NOT be interpreted as endorsement, priority, or correctness.

Organisation does not imply evaluation.

Change and Evolution Invariants

CM-CORPUS-INV-19 — Explicit Change Invariant

Any change to:

dimension set
dimension definitions
artefact–dimension assignments
projection rules

MUST be explicitly declared by the human curator.

Silent drift is prohibited.

CM-CORPUS-INV-20 — Backward Compatibility Invariant

Existing projections remain valid historical artefacts unless explicitly superseded.

New projections MUST NOT retroactively invalidate prior ones.

Summary for Human Readers

These invariants exist to ensure that the Cognitive Memoisation corpus:

remains navigable as it grows
can be read chronologically or thematically without confusion
preserves human authority over meaning and structure
avoids accidental reinterpretation by tooling or automation

They formalise how maps are drawn — not what the territory means.

Summary for Assisting Systems

When constructing corpus tables or projections:

do not invent
do not infer
do not optimise
do not rename
do not omit

Rearrange only what is already governed.

@@ Line 126: / Line 126: @@
 * 2026-01-04 — [[Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation]]
 * 2026-01-04 — [[Journey: Human-Led Convergence in the Articulation of Cognitive Memoisation]]
-* 2026-01-05 — [[Cognitive Memoisation for Governing Knowledge in Human–AI Collaboration]]
+* 2026-01-05 — [[Cognitive Memoisation for Governing Knowledge in Human-AI Collaboration]]
 * 2026-01-08 — [[Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study]]
 * 2026-01-08 — [[Authority Inversion: A Structural Failure in Human–AI Systems]]