Latest revision as of 23:49, 10 January 2026

metadata

Title:	Cognitive Memoisation: corpus guide.
Author:	Ralph B. Holland
version:	1.3.0
Publication Date:	2025-12-22T19:10Z
Update:	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 If any field is missing, unclear, or later superseded, the change MUST be made explicitly by the human and recorded via version update, not inferred.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.

Normative CM Paper References

The following documents constitute the authoritative CM corpus. Titles are normative MediaWiki page names and must not be paraphrased.

Dimensions Addressed in This Paper

The following dimensions are key to understanding the problems that Cognitive Memoisation (CM) addresses, especially in the context of stateless Large Language Models (LLMs) and the human-managed preservation of cognitive state:

1. Statelessness and Memory Management in LLMs

Core Concept: Addressing the statelessness of LLMs and the challenge of managing conceptual memory externally. Dimension Addressed: How can cognitive memory be maintained outside the LLM model to overcome statelessness, and how does CM provide this functionality while respecting LLM safety constraints? Relevant Papers:

2. Externalisation of Cognitive Artefacts

Core Concept: The process of externalising concepts, facts, inferences, and unresolved cognition into structured, durable formats. Dimension Addressed: How can cognitive content be externalised and stored in a manner that ensures its continued use across sessions, without being lost due to session termination or model limitations? Relevant Papers:

3. Round-Trip Knowledge Engineering (RTKE)

Core Concept: The cyclical process of taking externalised cognitive artefacts, reintegrating them into reasoning processes, and ensuring that knowledge evolves without loss. Dimension Addressed: How can externalised knowledge be reused, refined, and preserved over time through iterative processes, and how does CM facilitate this while maintaining consistency? Relevant Papers:

4. Dangling Cognates and Unresolved Cognition

Core Concept: Managing cognitive elements that are under construction or incomplete, allowing them to participate in reasoning without forcing premature resolution. Dimension Addressed: How can unresolved cognitive elements (Dangling Cognates) be preserved, tracked, and used safely in ongoing reasoning, without prematurely solidifying them? Relevant Papers:

5. Constraints and Knowledge Integrity

Core Concept: Defining and applying constraints to preserve the integrity of cognitive memory and prevent “Groundhog Day” rediscovery. Dimension Addressed: How can constraints be implemented to ensure that knowledge persists across sessions without redundancy, and how can it be efficiently reused? Relevant Papers:

6. Human Curated Knowledge vs. Model State

Core Concept: Differentiating between human-curated knowledge and LLM model state, ensuring that cognitive memory and decision-making remain under human control. Dimension Addressed: How can the human maintain full authority over cognitive content while ensuring that the stateless nature of LLMs is respected? Relevant Papers:

7. Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study

Core Concept: CM supports reflexive development, where knowledge evolves iteratively through Round-Trip Knowledge Engineering (RTKE). This process involves the externalisation, elaboration, and refinement of cognitive artefacts over time.
Dimension addressed: How does CM facilitate the continuous refinement of conceptual memory by allowing the re-integration of externalised cognitive artefacts, ensuring that knowledge development is adaptive and flexible?

Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study

8. Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct

Core Concept: Dangling Cognates, which are incomplete or evolving concepts, are treated as first-class cognitive constructs in CM. These cognitive elements are preserved and elaborated over time to allow for continuous cognitive development.
Dimension Addressed: How can CM manage and preserve Dangling Cognates, enabling humans to work with unresolved cognition and progressively refine and solidify these concepts across sessions without losing continuity?

Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct

9. ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering

Core Concept: The statelessness of LLMs, such as ChatGPT, leads to boundary friction in Round-Trip Knowledge Engineering (RTKE). LLMs do not retain memory across sessions, which causes knowledge loss between interactions.

Dimension Addressed: How can Cognitive Memoisation (CM) mitigate the friction caused by the stateless nature of LLMs by enabling the externalisation of cognitive artefacts, ensuring continuity of reasoning and the preservation of insights over time?

ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering

10. Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers)

Core Concept: Cognitive Memoisation (CM) must remain accessible to readers who are not specialists in knowledge engineering, modelling methodologies, or Large Language Model (LLM) systems, without diluting its epistemic discipline or human-governed stance.

Dimension Addressed: How can Cognitive Memoisation (CM) be explained in a clear, approachable manner that allows non-technical readers to understand its purpose, scope, and core mechanisms, while preserving the distinction between human-curated cognitive memory and stateless LLM behaviour?

Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers)

This paper serves as an accessibility bridge for the CM corpus. It provides a simplified conceptual explanation of CM’s goals, mechanisms, and governance principles without relying on specialised terminology, enabling broader understanding while remaining faithful to the core CM invariants.

11. Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct

Core Concept: Unresolved cognition is a persistent and necessary component of real-world reasoning. Cognitive Memoisation (CM) treats such incomplete or evolving concepts—Dangling Cognates—as first-class cognitive constructs rather than errors or temporary defects.

Dimension Addressed: How can Cognitive Memoisation (CM) preserve, track, and elaborate unresolved cognitive elements across Round-Trip Knowledge Engineering (RTKE) cycles, allowing them to participate safely in reasoning without being prematurely grounded or discarded?

Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct

This paper formalises Dangling Cognates within the CM framework. It explains how unresolved cognition can be externalised, carried forward, and progressively refined over time under explicit human governance, ensuring continuity of thought without forcing premature resolution.

12. Corpus alignment notes for Journey

The Journey paper already aligns strongly with the corpus guide’s “dimensions” framing, especially:
- Statelessness and Memory Management in LLMs
- Externalisation of Cognitive Artefacts
- Round-Trip Knowledge Engineering (RTKE)
- Human Curated Knowledge vs. Model State
The missing coverage above fills two gaps:
- An explicit “reader-facing” bridge paper (Plain-Language Summary)
- A DC-specific deepening paper (Dangling Cognates as first-class construct)
The corpus guide itself provides the canonical map of the corpus and is suitable as an explicit anchor reference for the Journey paper’s positioning.

The paper:

Journey:_Human-Led_Convergence_in_the_Articulation_of_Cognitive_Memoisation

13. Client-side Memoisation with the release of CM-2

This paper contains the disclosure of the CM-2 founding specification:
- client-side Memoisation
- Memoised Objects (EO)
- Attachment Objects (EA)
- Thought Bubbles as (UoD)

The paper:

Let's Build a Ship - Cognitive Memoisation for Governing Knowledge in Human - AI Collabortion

@@ Line 1: / Line 1: @@
-= Cognitive Memoisation: A Framework for Human Cognition =
+==metadata==
+<font size=-2>
+{|
+| ''Title'': || Cognitive Memoisation: corpus guide.
+|-
+|''Author'': || Ralph B. Holland
+|-
+| ''version'': || 1.3.0
+|-
+| ''Publication Date'': ||  2025-12-22T19:10Z
+|-
+| ''Update'':  || 2026-01-06T10:25Z v1.3.0 Includes the release of CM-2<br/>2025-01-04T05:12 v1.1.0 renamed from "Cognitive Memoisation: A framework for human cognition" to "Cognitive Memoisation: corpus guide"<br/>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
+|=
+| <br/>
+|}
+== Metadata (Normative) ==
-'''Author:''' Ralph B Holland
+The metadata table immediately preceding this section is CM-defined and constitutes the authoritative provenance record for this MWDUMP artefact.
-'''Date:''' 2025-12-23
-== Introductory Position ==
+All fields in that table (including artefact, author, version, date, local timezone, and reason) MUST be treated as normative metadata.
-This paper serves as the '''primary introduction and conceptual anchor''' for the Cognitive Memoisation (CM) corpus.
+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.
-Cognitive Memoisation is a human-governed knowledge-engineering pattern that preserves '''conceptual memory''' across interactions with stateless Large Language Models (LLMs). CM operates entirely outside model-internal memory, respecting architectural safety constraints while enabling humans to avoid repeated rediscovery (“Groundhog Day”) and to carry forward both resolved knowledge and deliberately unresolved cognition.
+<blockquote>
+This document predates its open licensing.
-This document establishes the rationale, scope, and interpretive frame required to correctly understand the remainder of the CM papers.
+As curator and author, I apply the [https://www.apache.org/licenses/LICENSE-2.0.txt|'''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]])</blockquote>
+</font>
+<div style="break-before:page"></div>
+= 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.
 == Normative CM Paper References ==
@@ Line 74: / Line 119: @@
 * [[Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation]] (Primary paper)
-== Role of This Paper in the Corpus ==
+=== 7. Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study ===
+'''Core Concept''': CM supports '''reflexive development''', where knowledge evolves iteratively through '''Round-Trip Knowledge Engineering (RTKE)'''. This process involves the '''externalisation''', '''elaboration''', and '''refinement''' of cognitive artefacts over time.
+<br/>'''Dimension addressed''': How does '''CM''' facilitate the '''continuous refinement of conceptual memory''' by allowing the '''re-integration''' of externalised cognitive artefacts, ensuring that knowledge development is adaptive and flexible?
+*  [[Reflexive Development of Cognitive Memoisation: A Round-Trip Cognitive Engineering Case Study]]
+=== 8. Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct ===
+'''Core Concept''': '''Dangling Cognates''', which are incomplete or evolving concepts, are treated as '''first-class cognitive constructs''' in '''CM'''. These cognitive elements are preserved and elaborated over time to allow for '''continuous cognitive development'''.
+<br/>'''Dimension Addressed''': How can '''CM''' manage and preserve '''Dangling Cognates''', enabling humans to work with '''unresolved cognition''' and progressively refine and solidify these concepts across sessions without losing continuity?
+* [[Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct]]
+=== 9. ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering ===
-This paper is authoritative for the following assertions:
+'''Core Concept''': The '''statelessness''' of '''LLMs''', such as '''ChatGPT''', leads to '''boundary friction''' in '''Round-Trip Knowledge Engineering (RTKE)'''. '''LLMs''' do not retain memory across sessions, which causes '''knowledge loss''' between interactions.
-* Cognitive Memoisation preserves '''conceptual-level memory''', not dialogue or model state.
+'''Dimension Addressed:''' How can '''Cognitive Memoisation (CM)''' mitigate the friction caused by the stateless nature of '''LLMs''' by enabling the '''externalisation''' of cognitive artefacts, ensuring continuity of reasoning and the '''preservation of insights''' over time?
-* CM exists specifically because LLMs are '''stateless by architecture'''.
+* [[ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering]]
-* CM externalises cognition into durable artefacts to enable '''Round-Trip Knowledge Engineering'''.
-* CM supports the deliberate carriage of unresolved cognition (Dangling Cognates).
+=== 10. Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers) ===
-* CM places authority, curation, and provenance '''entirely with the human'''.
+'''Core Concept''': Cognitive Memoisation (CM) must remain accessible to readers who are not specialists in knowledge engineering, modelling methodologies, or Large Language Model (LLM) systems, without diluting its epistemic discipline or human-governed stance.
+'''Dimension Addressed''': How can Cognitive Memoisation (CM) be explained in a clear, approachable manner that allows non-technical readers to understand its purpose, scope, and core mechanisms, while preserving the distinction between human-curated cognitive memory and stateless LLM behaviour?
+* [[Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers)]]
+This paper serves as an accessibility bridge for the CM corpus. It provides a simplified conceptual explanation of CM’s goals, mechanisms, and governance principles without relying on specialised terminology, enabling broader understanding while remaining faithful to the core CM invariants.
+=== 11. Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct ===
+Core Concept:
+Unresolved cognition is a persistent and necessary component of real-world reasoning. Cognitive Memoisation (CM) treats such incomplete or evolving concepts—Dangling Cognates—as first-class cognitive constructs rather than errors or temporary defects.
+Dimension Addressed:
+How can Cognitive Memoisation (CM) preserve, track, and elaborate unresolved cognitive elements across Round-Trip Knowledge Engineering (RTKE) cycles, allowing them to participate safely in reasoning without being prematurely grounded or discarded?
+* [[Reflexive Development of Cognitive Memoisation: Dangling Cognates as a First-Class Cognitive Construct]]
-All listed papers assume this framing and should be interpreted in relation to it.
+This paper formalises Dangling Cognates within the CM framework. It explains how unresolved cognition can be externalised, carried forward, and progressively refined over time under explicit human governance, ensuring continuity of thought without forcing premature resolution.
-== Main Paper ==
+=== 12. Corpus alignment notes for Journey ===
-The primary paper for understanding the full scope and technical details of Cognitive Memoisation is:
+* The Journey paper already aligns strongly with the corpus guide’s “dimensions” framing, especially:
+** Statelessness and Memory Management in LLMs
+** Externalisation of Cognitive Artefacts
+** Round-Trip Knowledge Engineering (RTKE)
+** Human Curated Knowledge vs. Model State
+* The missing coverage above fills two gaps:
+** An explicit “reader-facing” bridge paper (Plain-Language Summary)
+** A DC-specific deepening paper (Dangling Cognates as first-class construct)
+* The corpus guide itself provides the canonical map of the corpus and is suitable as an explicit anchor reference for the Journey paper’s positioning.
+The paper:
+* [[Journey:_Human-Led_Convergence_in_the_Articulation_of_Cognitive_Memoisation]]
-* [[Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation]]
+=== 13. Client-side Memoisation with the release of CM-2 ===
-== If you want next, I can: ==
+* This paper contains the disclosure of the CM-2 founding specification:
+** client-side Memoisation
+** Memoised Objects (EO)
+** Attachment Objects (EA)
+** Thought Bubbles as (UoD)
+The paper:
+* [[Let's Build a Ship - Cognitive Memoisation for Governing Knowledge in Human - AI Collabortion]]
-* Emit this as a '''strict MWDUMP-compliant page'''
+== categories ==
-* Add a '''categories block''' aligned to your wiki taxonomy
+[[Category:Cognitive Memoisation]] [[Category:Durable Knowledge]]
-* Produce a '''one-paragraph abstract''' for publication landing pages
+[[Category:Cognitive Memoisation]]
-* Design a '''front-matter template''' reused across all CM papers
+[[Category:Corpus Guides]]
-* Cross-check internal consistency of terminology across the listed documents
+[[Category:Plain-Language Summaries]]
+[[Category:Dangling Cognates]]
+[[Category:Round-Trip Knowledge Engineering]]
+[[Category:Epistemic Governance]]
+[[Category:Human–LLM Interaction]]
+[[category:LLM]]
+[[Category:Stateless Systems]]
+[[category:Ralph Holland:AI Publications]]
+[[category:memoisation]]
+[[category:public]]
+[[category:LLM]]
+[[category:ChatGPT]]
+[[Category:Human–AI Interaction]]
+[[Category:Stateless Systems]]
+[[category:Ralph Holland:AI Publications]]
+[[category:memoisation]]
+[[category:public]]