Cognitive Memoisation: corpus guide: Difference between revisions

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== Introductory Position ==
Introductory Position


This paper serves as the '''primary introduction and conceptual anchor''' for the Cognitive Memoisation (CM) corpus.
This paper serves as the primary introduction and conceptual anchor for the Cognitive Memoisation (CM) corpus.


Cognitive Memoisation is an accelerator for used in human-governed knowledge-engineering designed to preserve '''conceptual memory''' across interactions with stateless Large Language Models (LLMs). CM thus helps humans avoid repeated rediscovery (“Groundhog Day”) and to carry forward both resolved knowledge and deliberately unresolved cognition (Dangling Cognates).
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.


CM operates entirely outside model-internal memory while leveraging the power of LLM to infer postulates, perform stochastic pattern matching under 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.


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, which aligns with OpenAI's commitment to data protection. This safety mechanism prevents LLMs from making introspection or gaining agency in any form, ensuring that the model does not evolve autonomously or retain knowledge beyond the scope of 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.
 
Cognitive Memoisation (CM) bridges this lack of memory by allowing humans to externalise cognitive artefacts, preserving knowledge over time. This enables 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 LLMs are kept within their functional boundaries, executing only permitted tasks, with no capacity to alter their 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.
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.
In this version:
LLMs are presented as intentionally sandboxed, with no introspection or agency.
The human safety aspect is emphasized, ensuring the LLM cannot modify memory or execute non-permitted commands.
Cognitive Memoisation (CM) is framed as the solution to the lack of memory in stateless models, allowing humans to retain and build on their knowledge.


== Normative CM Paper References ==
== Normative CM Paper References ==

Revision as of 05:47, 23 December 2025

Cognitive Memoisation: A Framework for Human Cognition

Title: UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering
Author: Ralph B. Holland
version: 1.0.0
Publication Date: 2025-12-23
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 =

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:

Role of This Paper in the Corpus

This paper is authoritative for the following assertions:

  • Cognitive Memoisation preserves conceptual-level memory, not dialogue or model state.
  • CM exists specifically because LLMs are stateless by architecture.
  • CM externalises cognition into durable artefacts to enable Round-Trip Knowledge Engineering.
  • CM supports the deliberate carriage of unresolved cognition (Dangling Cognates).
  • CM places authority, curation, and provenance entirely with the human.

All listed papers assume this framing and should be interpreted in relation to it.

categories

Introductory Position

This paper serves as the primary introduction and conceptual anchor for the Cognitive Memoisation (CM) corpus.

Cognitive Memoisation is an accelerator for used in human-governed knowledge-engineering designed to preserve 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.

This document establishes the rationale, scope, and interpretive frame required to correctly understand the remainder of the CM papers.

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:

Role of This Paper in the Corpus

This paper is authoritative for the following assertions:

  • Cognitive Memoisation preserves conceptual-level memory, not dialogue or model state.
  • CM exists specifically because LLMs are stateless by architecture.
  • CM externalises cognition into durable artefacts to enable Round-Trip Knowledge Engineering.
  • CM supports the deliberate carriage of unresolved cognition (Dangling Cognates).
  • CM places authority, curation, and provenance entirely with the human.

All listed papers assume this framing and should be interpreted in relation to it.

categories

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