Context is Not Just a Window: Cognitive Memoisation as a Context Architecture for Human - AI Collaboration

Title:	Context Is Not Just a Window: Cognitive Memoisation as a Context Architecture for Human–AI Collaboration
Curator:	Ralph B. Holland
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
version:	1.0.0
Publication Date:	2026-01-0923:18

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Context Is Not Just a Window: Cognitive Memoisation as a Context Architecture for Human–AI Collaboration

Abstract

Large language models are commonly described as operating “in context,” yet what they actually consume is a finite, sliding window of tokens. This string-based mechanism bears some semblance to how humans maintain and use context—through concurrent, object-based, and selectively governed thought—but the similarity is superficial. As a result, human–AI collaboration is plagued not primarily by forgetting, but by misalignment of attention: some important ideas fall out of the token window while irrelevant ones persist, forcing the model to infer what the human still cares about from a decaying transcript.

By reframing context as a curated collection of identity-stable, time-anchored objects rather than a linear string, Cognitive Memoisation enables reliable continuity, prevents semantic drift, and makes distributed cognition computationally tractable.

1. Introduction

Governed, selected Epistemic Objects guide the expansion of cognition.

Human thinking does not unfold over undifferentiated text. It unfolds over a working set of ideas that are brought forward, held in view, and intentionally revisited while others are set aside. We reason by selecting what matters, returning to it, and allowing new ideas to grow around it.

Large language models, however, are asked to reason over a continuously shifting string of tokens. Their “context” is not a cognitive workspace but a mechanical buffer: whatever happens to be within the window is all that exists. Relevance, priority, and persistence must be inferred rather than chosen.

This mismatch is the core failure of human–AI collaboration today. When a human moves across multiple lines of thought, revisits earlier ideas, or temporarily explores side paths, the model cannot tell which of those tokens are still alive and which have become background. Important ideas disappear while irrelevant ones linger, not because the model is forgetful, but because it has no notion of what is being intentionally held.

2. The Myth of Context in Language Models

The term context suggests continuity, memory, and relevance. In practice, it refers only to a sliding window of tokens that is reprocessed on every turn. Nothing inside that window has identity, history, or status. It is merely a sequence.

This is sufficient for pattern completion. It is not sufficient for collaborative thought.

Humans do not reason over sequences. They reason over objects: ideas, hypotheses, drafts, questions, and goals. These objects persist even when not currently in view. They have relationships, histories, and roles in a larger line of reasoning.

When models are forced to infer those objects from a transcript, collaboration becomes fragile. The model must guess what matters based on position and frequency, rather than being told what is being held in the mind.

3. Thought as Context

An Epistemic Object is not text in a buffer. It is a named, identity-stable representation of an idea that can be returned to, revised, or set aside. Epistemic Attributes govern how that object is to be interpreted: whether it is tentative or settled, in scope or out of scope, protected or discardable.

Multiple objects can be active at once, forming a Universe of Discourse—a bounded thought bubble corresponding to a particular line of reasoning.

In this architecture, context is no longer whatever happens to remain in view. It is the set of objects that have been deliberately selected to be active.

4. Selection, Not Accumulation

A token window grows by accumulation. A cognitive workspace grows by human selection.

When everything that has been said is treated as equally relevant, attention is diffused and meaning drifts. When only certain objects are kept active—because a human has chosen them—cognition becomes focused and directed.

This is the key difference between remembering and thinking.

5. Context as a Shared Cognitive Workspace

By giving humans and machines access to the same set of selected thoughts, Cognitive Memoisation creates a shared workspace in which both can operate. The model no longer has to infer what the human is thinking from a transcript; it is given the objects that the human has chosen to keep alive.

This makes it possible for multiple lines of thought to coexist, ideas to be revisited after long gaps, and complex projects to evolve without semantic collapse.

Context becomes something that is curated, not something that simply happens.

6. Implications

When context is treated as a curated collection of thoughts rather than a sliding string, human–AI collaboration changes fundamentally. Machines no longer have to infer what a human is thinking from a decaying transcript; they are given the set of ideas that the human has chosen to keep alive.

This shift makes it possible for concurrent lines of reasoning, long-running projects, and evolving questions to coexist without collapsing into confusion or drift. Attention becomes directed rather than accidental, and continuity becomes something that is maintained rather than guessed.

Cognitive Memoisation is the architecture that makes this possible. It provides a way for human-selected thoughts to persist, to be revisited, and to guide the expansion of shared cognition across time, sessions, and systems.

The context window remains finite. Meaning does not have to be.

7. Cognitive Memoisation

Cognitive Memoisation is a way of making thought persist without turning it into memory or dogma. Instead of treating everything that has been said as equally relevant, it allows people to choose which ideas remain active and which can be set aside.

In practice, this means that ideas, questions, drafts, and conclusions are treated as distinct, named objects rather than as fragments of a transcript. These objects can be returned to, refined, compared, or temporarily ignored, while still remaining available for future use. What matters is not how recently an idea was mentioned, but whether it has been deliberately kept in view.

This makes it possible to work across time and across lines of thought without losing coherence. A person can explore side paths, pause a project, or switch topics entirely, and later return to the same set of ideas without having to reconstruct them from memory or from a long conversation history.

Cognitive Memoisation does not decide what is true or important. It only provides the means for humans to make those decisions explicit, and for machines to respect them. In doing so, it turns collaboration from a fragile interaction with a fading transcript into an ongoing process built on shared, selectable thought.

8. Cognitive Memoisation in Practice

8.1 Distributed Cognition

Complex work is rarely done by a single mind. Designing a ship, writing a legal code, or building a software system involves many people, each contributing partial knowledge, sketches, calculations, and judgments. What makes this possible is not shared memory, but shared representations: plans, diagrams, specifications, and drafts that everyone can see, modify, and refer back to.

In this sense, cognition is already distributed across people and artefacts. Cognitive Memoisation extends this pattern into human–AI collaboration. Selected thoughts become the shared objects that both humans and machines work over, allowing a single architect, or many collaborators, to guide a growing body of ideas without losing control of its structure.

8.2 RT-KT

Much of what people want from AI is not the generation of new images or clever prose, but the ability to regurgitate, inspect, and refine their own thinking. They want to see their ideas reflected back, sharpened, rearranged, and tested.

Cognitive Memoisation supports this directly. Because thoughts are captured as explicit objects, they can be compared, edited, challenged, or extended. The system does not need to guess what the user meant earlier; it is given the actual ideas that are being worked on. This makes iteration faster, clearer, and far less prone to drift.

8.3 The Bridge Between Humans, LLMs, and Formal Systems

Humans think in terms of ideas and intentions. LLMs operate on token streams. Formal systems require structured, unambiguous representations. Cognitive Memoisation provides a bridge between these worlds.

Selected thoughts can be expressed in human language, manipulated by an LLM, and mapped into formal structures when needed. The same underlying objects can support brainstorming, rigorous analysis, and automated processing, without losing their identity or meaning along the way.

9. Propositions

The following propositions are offered not as doctrine, but as objects for thought. They are meant to be examined, challenged, extended, and combined. Together they describe three orthogonal dimensions of Cognitive Memoisation: practice, governance, and theory.

9.1 Practice Propositions

• Rediscovery is the default without externalisation.
• Fluency creates a false sense of agreement.
• Meaning drifts without checkpoints.
• Context collapses without boundaries.
• Humans assume continuity even when none exists.
• Distributed work fails when shared representations are not explicit.
• Tools and people must coordinate through external artefacts, not memory.

9.2 Governance Propositions

• Continuity is automatic; authority is human.
• Nothing becomes authoritative without explicit human action.
• Aggregation has no epistemic force.
• All cognition must have identity and time.
• Platforms must be epistemically neutral.
• Externalised objects are not memory or model state.
• Governance must apply to representations, not to people or machines.
• Knowledge must be inspectable, auditable, and revisable without reinterpretation.

9.3 Bridge Propositions

• Cognitive Memoisation provides a substrate for distributed cognition.
• It allows cognition to be shared without surrendering authority.
• It enables institutional memory without institutional ossification.
• It makes collective intelligence auditable and portable.
• It supports geometric expansion of ideas.
• Selected thoughts become the representational media of shared cognition.
• Governance becomes the rule system of that cognition.
• Thought bubbles become activity systems.
• Deliberate saving becomes the creation of durable knowledge.

10. Conclusion

The context window remains finite. Meaning does not have to be.

Human–AI collaboration fails today not because machines are weak, but because the architecture of context is wrong. By treating thought as something that can be selected, preserved, and shared rather than something that merely happens to remain in view, Cognitive Memoisation makes it possible for ideas to grow without losing their shape.

This does not solve every problem of intelligence. It solves the problem of keeping thinking intact long enough for intelligence—human or artificial—to do its work.

References

Cognitive Memoisation Corpus (MediaWiki pages)

• Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers)
• Progress Without Memory: Cognitive Memoisation as a Knowledge-Engineering Pattern for Stateless LLM Interaction
• Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation
• Journey: Human-Led Convergence in the Articulation of Cognitive Memoisation
• Let's Build a Ship - Cognitive Memoisation for Governing Knowledge in Human - AI Collaboration
• Cognitive Memoisation (CM) Public Statement and Stewardship Model

Distributed Cognition (External Academic Sources)

• Hutchins, Edwin (1995). Cognition in the Wild. MIT Press.

• Hutchins, Edwin (2000). Distributed Cognition. In International Encyclopedia of the Social and Behavioral Sciences, pp. 2068–2072. Elsevier. DOI: 10.1016/B0-08-043076-7/01636-3