Mechanical Extraction of Thought: Bootstrapping Epistemic Objects from Sequential Input under Cognitive Memoisation

metadata

Title:	Mechanical Extraction of Thought: Bootstrapping Epistemic Objects from Sequential Input under Cognitive Memoisation
Curator:	Ralph B. Holland
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
Version:	1.0.0
Publication Date:	2026-01-12T23:23Z
Updates:	2026-01-13T19:22Z 1.0.0 - completed DC and EO discussion.
Binding:	final

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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.

Mechanical Extraction of Thought: Bootstrapping Epistemic Objects from Sequential Input under Cognitive Memoisation

Abstract

Thought can be mechanically extracted from sequential input into Epistemic Objects (EO). These EO may be categorised using Epistemic Attributes (EA), providing Thought to be staged in ephemeral form and stabilised without inferring authority. This supports reuse of meaning as semantic drivers for temporally and spatially decoupled interaction, providing round-trip knowledge engineering and distributed cognition under Cognitive Memoisation (CM-2).

1. Introduction

Interactive and conversational LLM platforms fail at long-horizon knowledge work because their context mechanisms are shallow, transient, and incapable of reliably extracting, retaining, or reintroducing epistemic data arising from human cognition. Thought is silently dropped as context shifts, depth is limited by window constraints, and there is no principled way to re-prime lost material into the current inference surface without re-authoring it.

This paper examines how Thought arising in a human input stream can instead be normatively constrained into Epistemic Objects (EO), with Epistemic Attributes (EA) providing provisional structure, so continuity is mechanically assisted while authority, stability, and promotion remain explicitly human under Cognitive Memoisation (CM-2).

2. Problem Statement: Context Is Not Cognition

Conversational context in interactive LLM platforms is structurally incapable of supporting cognition over time. Context is shallow, transient, and authority-blind: it does not distinguish Thought from phrasing, importance from recency, or conclusion from exploration. As interaction progresses, epistemic material is silently dropped without signal, boundary, or audit, producing loss that is indistinguishable from completion.

There is no native mechanism to preserve depth, no marker for what must persist, and no principled way to re-prime absent Thought back into the active inference window without re-authoring it. Reasoning remains locally coherent but globally fragile, repeatedly re-entering prior abstractions without accumulation. This is not a failure of intelligence or fluency, but of context itself.

3. Normative Framing: Thought as Extractable Epistemic Material

Thought arising during human interaction with a platform is treated here not as an emergent property of dialogue, but as epistemic material that can be normatively identified, bounded, and collected from a sequential input stream. Importance is not inferred from fluency, recency, or repetition.

Claims, constraints, definitions, arguments, relationships, unresolved cognates, and compound semantic structures are all eligible material. These are externalised as Epistemic Objects (EO), while Epistemic Attributes (EA) qualify scope, provisionality, or lifecycle without asserting authority. Collection is governed by declared invariants rather than heuristics.

4. Mechanism: Normative Collection of Epistemic Objects from Sequential Input

Normative collection treats the human input stream as ordered epistemic material rather than conversational residue. Sequential input is processed under declared invariants that specify how units of meaning may be bounded and identified without inferring correctness, authority, or durability.

Epistemic Objects are not limited to atomic fragments. An EO may encapsulate any semantically meaningful structure expressible in token-parsable form, including dense arguments, concept networks, mixed relational structures, or extended prose. EO govern identity and lifecycle, not internal semantic shape.

The mechanism is normative rather than algorithmic. No promotion by fluency, no persistence by repetition, and no aggregation by implication are permitted. Loss of context does not imply resolution.

Candidate Thought Bubbles are proposed by grouping EO that participate in the same local line of reasoning. Thought Bubbles are analogous to threads but are governed explicitly via Epistemic Attributes rather than inferred from turn order or recency.

EO population is continuous. New EO arise through interaction; revisions create new EO identities where content materially changes; prior EO may be rehydrated into the working surface without semantic alteration. Accumulation supports continuity only.

Mechanical assistance may propose EO, EA, and Thought Bubble boundaries, but all proposals are non-authoritative. The human governs refinement, aggregation, discard, and promotion.

5. EO Taxonomy and Identity Across Surfaces

The same Epistemic Object may exist across multiple surfaces without creating different objects.

EOm: the Epistemic Object as held in human cognition. EOs: the Epistemic Object as expressed on the session interaction surface. EOc: the Epistemic Object serialised into a client-side cache to support continuity. EOe: the Epistemic Object externalised into an external, durable store.

These are identity-related representations of the same EO. Transitions between surfaces are changes of substrate, not of epistemic identity.

Governance over EO scope, lifecycle, provisionality, and transition is expressed through Epistemic Attributes (EA), not inferred from persistence or form.

Side note (normative intent): Any client-side cached Epistemic Object (EOc) must be exportable to an external durable form (EOe) in a text-parseable, portable format compliant with CM-2. Vendor-internal representations are unconstrained, but faithful, non-reinterpretive export is mandatory.

6. Epistemic Attributes as Provisional Structure

Epistemic Attributes (EA) provide provisional structure without authority. They qualify how an Epistemic Object is to be interpreted, reused, or constrained without asserting correctness, durability, or truth. EA do not exist to stabilise meaning; they exist to govern interpretation.

An EA may express, among other things:

• scope limitations,
• uncertainty or confidence bounds,
• intended audience or use,
• lifecycle state (exploratory, provisional, deprecated),
• contextual dependencies,
• temporal relevance,
• compatibility constraints,
• governance notes.

Crucially, EA are non-promotable in isolation. An EA cannot become authoritative independently of an EO. If an attribute acquires enduring significance, it MUST be explicitly elevated into a new EO by human action.

EA may be ephemeral or persistent, but persistence does not imply authority. An EA may persist across sessions to prevent misuse or misinterpretation of an EO without elevating that EO to canonical status.

EA act as epistemic guardrails. They prevent silent reinterpretation by constraining how EO may be rehydrated into new contexts. Unlike conversational qualifiers (“roughly”, “I think”, “probably”), EA are explicit, inspectable, and govern reuse rather than tone.

Mechanical systems MAY propose EA based on declared invariants or detected conditions, but EA proposals are always advisory. Acceptance, modification, or rejection remains explicitly human.

7. Generative Interaction and Cache Rehydration

Generative interaction under CM-2 does not assume continuity of context. Continuity is achieved through rehydration of Epistemic Objects into the active inference surface.

Rehydration is the act of making an EO available for inference without modifying its identity, content, or governance state. Rehydration is not recall and not memory; it is deliberate re-presentation.

Client-side memoisation (EOc) supports this process by maintaining a cache of EO identities that can be reintroduced when relevant. This cache is authority-neutral. It exists to reduce rediscovery, not to imply agreement or permanence.

Rehydration MUST be:

• identity-stable (the same EO remains the same object),
• non-mutating (content is not rewritten),
• scoped (only EO relevant to the current Thought Bubble are introduced),
• auditable (the source and state of EO are visible).

Generative output MAY build upon rehydrated EO, but any material change results in the creation of a new EO, not mutation of the prior one. Revision produces lineage, not overwrite.

This separation ensures that fluency does not masquerade as continuity and that generation does not silently rewrite prior cognition.

8. Failure Modes Without Governance

Absent governance, conversational systems exhibit predictable epistemic failure modes:

• Silent Loss — Important thought disappears as context rolls, indistinguishable from completion.
• Rediscovery Loops — Prior conclusions are re-derived without recognition.
• Authority Inversion — Persistent phrasing is mistaken for commitment.
• Semantic Drift — Meaning changes incrementally without detection.
• Context Bleed — Assumptions leak across unrelated lines of reasoning.
• Accidental Canonisation — Exploratory output becomes treated as settled knowledge.

These failures are not defects of generation quality. They arise because conversational context has no epistemic semantics. It cannot distinguish provisional from durable, important from incidental, or concluded from unresolved.

Mechanical extraction of EO interrupts these failures by making epistemic material explicit, bounded, and governable. Loss becomes visible. Drift becomes detectable. Authority becomes deliberate.

9. Implications for Round-Trip Knowledge Engineering

Round-Trip Knowledge Engineering (RTKE) requires that knowledge survive beyond any single interaction while remaining governable. Mechanical extraction of EO enables RTKE by ensuring that:

• epistemic material is externalised,
• identity is preserved across sessions,
• reuse does not imply authority,
• promotion is explicit and human-initiated.

EO serve as the semantic carriers for RTKE. They allow knowledge work to progress across time and tools without relying on fragile conversational continuity.

Because EO are text-parseable and portable, they may be re-ingested across different platforms, models, or interfaces without reinterpretation. This enables vendor-neutral knowledge workflows and supports audit, review, and recovery.

10. Distributed Cognition and Client-Side Custody

Mechanical extraction of EO supports distributed cognition by recognising that cognition is not confined to a single mind, session, or system.

Client-side custody of EO is critical. The client, not the model, is the appropriate locus for continuity. Models remain stateless and non-authoritative; clients manage identity, rehydration, and export.

This inversion avoids platform lock-in and prevents control-plane or UI-layer failures from corrupting epistemic state. The model is treated as a compute surface, not a repository.

Distributed cognition emerges when multiple humans, tools, or sessions operate over shared EO under shared governance, without assuming shared memory or implicit agreement.

11. Discussion and Limitations

Mechanical extraction of Thought does not solve all problems of knowledge work.

It does not guarantee correctness, truth, or consensus. It introduces procedural overhead and requires discipline. It may feel slower than informal conversation, particularly for casual tasks.

CM-2 explicitly trades convenience for auditability, continuity, and authority preservation. This trade-off is intentional and context-dependent.

The approach assumes a human willing to curate, govern, and promote knowledge deliberately. It is unsuitable where automation, speed, or implicit accumulation are the primary goals.

12. Conclusion

Thought can be mechanically extracted from sequential human input into Epistemic Objects without inferring authority, correctness, or permanence. Epistemic Attributes provide provisional structure, while Cognitive Memoisation (CM-2) governs continuity, identity, and promotion.

This separation allows interactive systems to support long-horizon knowledge work without pretending to remember, learn, or decide. Continuity is assisted; authority remains human.

By treating Thought as extractable epistemic material rather than conversational residue, we enable round-trip knowledge engineering, distributed cognition, and recovery from failure—without granting agency to machines or collapsing deliberation into habit.

Progress is achieved not by giving systems memory, but by governing what must not be forgotten.

References

Holland, R. B. (2025). Progress Without Memory: Cognitive Memoisation as a Knowledge-Engineering Pattern for Stateless LLM Interaction - (CM-1).

https://publications.arising.com.au/pub/Progress_Without_Memory:_Cognitive_Memoisation_as_a_Knowledge_Engineering_Pattern_for_Stateless_LLM_Interaction

Holland, R. B. (2026). Cognitive Memoisation for Governing Knowledge in Human - AI Collaboration - (CM-2).

https://publications.arising.com.au/pub/Cognitive_Memoisation_for_Governing_Knowledge_in_Human_-_AI:_Collaboration

Holland, R. B. (2026). Authority Inversion: A Structural Failure in Human–AI Systems.

https://publications.arising.com.au/pub/Authority_Inversion:_A_Structural_Failure_in_Human–AI_Systems

Holland, R. B. (2026). Durability Without Authority: The Missing Governance Layer in Human-AI Collaboration.

https://publications.arising.com.au/pub/Durability_Without_Authority:_The_Missing_Governance_Layer_in_Human-AI_Collaboration

Holland, R. B. (2026). Why Cognitive Memoisation Is Not Memorization

https://publications.arising.com.au/pub/Why_Cognitive_Memoisation_Is_Not_Memorization

CM-2 guidance

See Cognitive Memoisation for Governing Knowledge in Human - AI Collaboration CM-2 for normative invariants involving EO.