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

Title:	ChatGPT UI Boundary Friction as a Constraint on Round-Trip Knowledge Engineering
Author:	Ralph B. Holland
Version:	1.3.2
Editorial Update:	Included BF-8, BF-9, BF-7 addendum, and BF-10 (False Liveness via Implied Asynchrony); clarified synchronous execution semantics and added a normative invariant prohibiting implied asynchrony; added postulate on client-scoped inference streams within a logical session.
Publication Date:	2025-12-20T10:28Z
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
Provenance:	This is an authored paper maintained as a MediaWiki document; reasoning across sessions reflects editorial changes, not collaborative authorship.
Governance:	MWDUMP (authoritative)
Method:	Cognitive Memoisation (CM)
Status:	final

Metadata (Normative)

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

All fields in that table (including title, curator/author, affiliation, contact, version, update history, publication date, and binding status) MUST be treated as normative metadata.

The assisting system MUST NOT infer, normalise, reinterpret, duplicate, or rewrite these fields. Any change to metadata MUST be made explicitly by the human and recorded via a versioned update, not inferred.

Curator Provenance and Licensing Notice

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.

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

Abstract

Large Language Models (LLMs) are stateless across sessions, leading to repeated rediscovery of concepts, constraints, and failure modes during extended cognitive work. This paper examines a distinct but under-analysed source of cognitive loss: user-interface boundary friction occurring when human intuition and LLM inference are otherwise well aligned.

Using Cognitive Memoisation (CM) as the governing knowledge-engineering pattern, the work externalises invariants, constraints, and interaction conventions into authoritative artefacts (MWDUMP) that govern subsequent reasoning without reliance on dialogue history or model memory. Boundary failures observed during live work—rendering instability, session degradation, artefact expiry, and recovery incoherence—are analysed as first-class epistemic events rather than usability defects.

The paper documents a novel anomaly in which UI failure exposed parallel inference streams from identical conversational groundings, demonstrating that trust erosion arises not from stochastic inference itself, but from unmanaged interface behaviour during degraded states.

This paper is reflexive: Cognitive Memoisation was used to sustain and recover the very work analysing failures that would otherwise have terminated it, demonstrating CM as a practical mechanism for cross-session round-trip knowledge engineering under UI degradation.

Canonical Thesis

When human intuition and LLM inference are well-aligned, the dominant sources of cognitive loss are not errors in reasoning, but failures and revelations at the user–interface boundary. These include both *boundary friction*, which impedes progress, and *boundary revelation*, which unexpectedly exposes latent properties of stochastic inference.

Scope

This paper examines extended, high-cognition human–LLM collaboration conducted using the Cognitive Memoisation (CM) pattern across multiple stateless sessions. It analyses observed UI boundary failures and stochastic inference exposure events as first-class knowledge artefacts, using MWDUMP as the governing invariant.

Key Claim

Cognitive delay, loss of flow, and trust disruption arose primarily from interface-mediated effects — not from misunderstanding, hallucination, or model error — and these effects materially slowed progress during otherwise productive round-trip knowledge engineering.

Methodological Context

• Cognitive Memoisation (CM) governed all work.
• Progress was achieved across multiple stateless sessions through explicit re-activation of MWDUMP artefacts.
• Reasoning proceeded via round-trip engineering between:

 - human intuition,
 - LLM inference,
 - and externalised authoritative artefacts.

• UI behaviour was not under test; boundary effects were discovered incidentally during legitimate modelling work.
• System-level observations (e.g. host load) were used to corroborate UI degradation effects.

Boundary Failure Classes (UI Friction)

BF-1: Output Channel Interpretive Instability

MediaWiki-formatted CM artefacts were inconsistently rendered as:

• markdown,
• prose,
• or partially interpreted hybrid output.

This instability required defensive strategies, including forced code-wrapping, to preserve artefact fidelity. The behaviour was undocumented and unpredictable from the user perspective.

Human Impact: Repeated reformatting interrupted cognitive flow and imposed vigilance costs during deep conceptual work.

BF-2: Progressive Session Degradation Under Artefact Load

As CM and MWDUMP artefacts accumulated:

• rendering latency increased,
• keystrokes were intermittently dropped,
• cursor positioning became unreliable,
• and perceived UI responsiveness degraded non-linearly.

These effects correlated with sustained browser renderer load, as observed via host system monitoring (e.g. htop).

Inference: UI degradation is likely cumulative rather than threshold-triggered, with artefact volume acting as a stressor.

BF-3: UI Stall ("Grip") and Partial Recovery Failure

The UI entered a stalled state characterised by:

• delayed or truncated output,
• failure to complete responses,
• absence of expected continuation markers.

Recovery required a manual browser refresh, resulting in partial rather than clean session recovery.

BF-4: Artefact Lifecycle Opacity

Uploaded artefacts appeared valid until accessed, at which point expiry or failure was revealed. Re-uploading identical artefacts did not reliably clear the condition.

This behaviour forced premature extraction and defensive parking of knowledge via MWDUMP.

BF-5: Turn Capacity Insufficiency

The conversational turn input channel permits construction and pasting of long, complex text artefacts, but does not reliably support their submission as a single coherent unit. Capacity limits are enforced only at commit time, resulting in rejection of otherwise valid artefacts with minimal diagnostic information.

This behaviour directly constrains the expression of complex, structured cognitive artefacts required for Cognitive Memoisation (CM). Long-form reasoning, provenance annotation, predicate grouping, and temporal ordering cannot be safely represented when a turn cannot hold the complete artefact.

As a result, cognition is prematurely fragmented along undocumented system boundaries rather than semantic or epistemic ones. This forces defensive strategies such as manual chunking, external parking, or early extraction into MWDUMP format, increasing interference and risk of artefact degradation.

Unlike boundary revelations that merely expose latent system behaviour, this condition actively impedes round-trip knowledge engineering by restricting what can be materially committed. The turn input functions as a narrow channel optimised for short exchanges, rather than as an episodic container suitable for artefact-based cognition.

This is classified as Boundary Friction because it directly interferes with reasoning, submission, and verification workflows, rather than simply revealing hidden system characteristics.

BF-6: UI Deadlock with Silent Loss of Post-Exchange Human Cognitive Work

During intensive Cognitive Memoisation (CM) sessions involving repeated CM Round-Trip Knowledge Exchanges (R-T KEs), the user interface may enter a persistent busy or loaded state.

In this state:

• The UI displays an indefinite enter narattive state (audio)
• All human input events are ignored, including typing, clicking, navigation, and cancellation attempts.
• Closing the browser tab or apparent session connection in an attempt to recover the session, fails with session restored at identical frozen state.
• Opening the same session in a fresh browser, a different browser profile, or on a different machine reproduces the same frozen state, indicating server-side persistence.

The CM exchange completes computationally and produces readable output, but no explicit commit boundary is crossed.

As a result, all human cognitive work performed after the last committed of the CM Round-Trip-Knowledge-Enginering is unrecoverably lost. This includes human verification, correction, restructuring, provenance alignment, and semantic refinement.

Human Impact: The loss is not limited to conversational context or text. It represents the loss of human cognitive labour performed after the final CM exchange. This work may have been substantial, verified, and internally coherent, yet disappears without warning or error.

This failure mode is worse than a "Groundhog Day" repetition scenario. In a Groundhog Day loop, the human is aware that work must be repeated. In this case, the UI presents false continuity while silently discarding all post-exchange human effort, eliminating both the work itself and the opportunity to reliably reconstruct it.

Inference: This represents a commit-boundary failure rather than a reasoning or generation failure. The system produces valid CM output, but fails to durably memoise subsequent human cognitive contributions. The UI enters a non-progressable state without any option to progress.

BF-7: Cross-Sessional Stickiness and the Illusion of Residual Cognitive State

A further form of boundary friction arises from what may be termed cross-sectional stickiness: the persistence of artefact-handling behaviour across interactions despite the execution of all user-accessible isolation and state-reset mechanisms, including those intended to establish a provably clean execution context.

In the observed case, the author conducted a controlled isolation test explicitly to determine whether artefact fileup and ingestion would succeed in a clean environment. The following actions were performed as experimental controls:

• delete all chat sessions, disable (MCS) memory, initiate a new session and attempt file upload
• Creation of a new ChatGPT account, disable (MCS) memory, initiate a new session and attempt file upload

These actions were undertaken not as remediation, but to falsify the hypothesis that prior account state, conversational history, or memory settings were responsible for the observed artefact handling failure.

Result: The fileupload failed to produce a usable or accessible artefact. The system continued to present signals consistent with file unavailability or expiry, without any authoritative indication (from the UI) that ingestion had succeeded or failed in the new context.

Crucially, the system provided no observable distinction between:

• artefact handling in the original account,
• artefact handling in the newly created account,
• or first-time artefact ingestion in a clean session.

No diagnostic signal allowed the author to confirm that account-level isolation, memory disablement, or session renewal altered the execution context in any meaningful way with respect to artefact lifecycle.

This persistence eliminates explanations based solely on conversational continuity, memory retention, or overlapping sessions, without requiring any assumptions regarding backend architecture, IP-based constraints, caching layers, or UI implementation. Classification is based strictly on observable system behaviour and failed experimental isolation.

The epistemic harm arises from the system’s inability to provide a certifiable clean baseline. Users are led to infer the presence of hidden memory, contamination, or residual cognitive state, when the underlying issue instead lies in opaque artefact-handling conditions that are neither inspectable nor invalidatable through any exposed interface.

From a knowledge-engineering perspective, this represents a failure of falsifiability and observability rather than a failure of user action or reasoning. When controlled isolation tests cannot change or reliably probe system behaviour, invariants cannot be established and provenance chains cannot be closed.

In the context of round-trip knowledge engineering, this form of boundary friction is particularly severe. The inability to certify a clean execution context prevents the establishment of reliable invariants and compromises workflows that depend on explicit provenance, externalized authority, and controlled re-ingestion of artefacts. The resulting ambiguity reflects a structural misalignment between session semantics, resource lifecycle management, and user-facing guarantees, rather than any defect in user behavior.

analysis by chatGPT LLM follows

BF-7: Groundings and Type Implications

BF-7 is grounded not in system internals, but in observable epistemic properties of interaction. Its implications arise from violations of specific grounding assumptions and induce type-level failures in round-trip knowledge engineering.

Grounding Assumptions (Required for Valid Operation)

The following groundings are implicitly required for governed Cognitive Memoisation and round-trip knowledge engineering:

G-1

Isolation Grounding

User-accessible isolation mechanisms (session renewal, account recreation, memory disablement) are assumed to bound artefact-handling behaviour.

G-2

Observability Grounding

The system is assumed to expose sufficient signals for a user to determine whether an artefact has been newly and durably ingested.

G-3

Falsifiability Grounding

Users are assumed to be able to falsify hypotheses about prior state influence through controlled action.

G-4

Provenance Grounding

Artefact lifecycle state is assumed to be traceable and distinguishable across ingestion attempts.

BF-7 arises when these groundings are violated in combination.

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Type Implications

Violation of the above groundings induces the following type-level failures.

T-1

Execution Context Ambiguity

The execution context becomes an untyped or ambiguously typed state. A session cannot be classified as clean, contaminated, or first-use.

T-2

Artefact State Collapse

Artefacts collapse from a typed lifecycle state (e.g. {new, ingested, expired, invalid}) into an untyped or opaque condition. State distinctions exist operationally but are not observable.

T-3

Isolation Control Failure

User actions intended as isolation controls lose their discriminative power. Account creation, memory disablement, and session renewal no longer function as meaningful type constructors.

T-4

Hypothesis Non-Falsifiability

Hypotheses regarding causal influence of prior state become non-falsifiable. This converts engineering investigation into speculation.

T-5

Provenance Break

Provenance chains cannot be closed because artefact origin, ingestion attempt, and acceptance status cannot be typed or certified.

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Epistemic Consequences

As a result of the above type failures:

• Residual state is inferred without evidence.
• Memory is misattributed where only opaque resource persistence may exist.
• Trust is degraded not by incorrect inference, but by unclassifiable system behaviour.
• Cognitive Memoisation governance cannot assert authority over re-ingested artefacts.

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BF-7 Classification Rule

If user actions that are intended to function as isolation or reset operators fail to produce observably distinct artefact-handling outcomes, then the system enters a BF-7 state.

In a BF-7 state:

• Execution contexts are not reliably typed.
• Artefact ingestion cannot be certified.
• Knowledge-engineering invariants cannot be enforced.

This classification holds independently of implementation, platform, or root cause.

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Boundary Friction: BF-8 and BF-9

Context and Motivation

During sustained Cognitive Memoisation (CM) and Round-Trip Knowledge Engineering (RT-KE) work, repeated UI pathologies were observed that could not be explained by inference failure, model error, or user error. These failures manifested as apparent stalls, missing outputs, or UI freezes, despite strong evidence that inference had completed successfully.

To prevent rediscovery of these failure modes (“Groundhog failures”), the observations were progressively externalised as Boundary Friction (BF) artefacts.

A key refinement step occurred when the author deliberately captured multiple BF observations in TOML, colocated within a single artefact, each under its own heading. This enabled structured comparison within a single canonical representation, rather than relying on memory, narrative, or cross-session recall.

Method: Capturing by TOML Stratification

Instead of treating each observation as an isolated anecdote, the author:

• Encoded each Boundary Friction instance as a separate TOML block, each explicitly labelled (e.g. BF-7, BF-8, BF-9).
• Embedded these TOML blocks under distinct headings within the same governing artefact.
• Ensured that each TOML block preserved:
  • scope,
  • classification,
  • observed symptoms,
  • inferred causal boundary,

without forcing premature consolidation.

This approach intentionally allowed coexistence without collapse: multiple descriptions of failure could sit side-by-side, making divergence (if any) visible rather than implicit.

Context and Motivation

During sustained Cognitive Memoisation (CM) and Round-Trip Knowledge Engineering (RT-KE) work, repeated UI pathologies were observed that could not be explained by inference failure, model error, or user error. These failures manifested as apparent stalls, missing outputs, or UI freezes, despite strong evidence that inference had completed successfully.

To prevent rediscovery of these failure modes (“Groundhog failures”), the observations were progressively externalised as Boundary Friction (BF) artefacts.

A key refinement step occurred when the author deliberately captured multiple BF observations in TOML, colocated within a single artefact, each under its own heading. This enabled structured comparison within a single canonical representation, rather than relying on memory, narrative, or cross-session recall.

BF-8: Rendering-Layer Saturation with Inference Completion

BF-8 characterises a failure mode where model inference completes successfully, but the client-side rendering or presentation layer becomes saturated or stalled.

Observed characteristics include:

• Model inference completes correctly.
• Session state remains valid.
• The UI exhibits high latency, apparent freezing, or “grip”.
• Fans may spin and local resource usage may spike.
• Rendered output is missing, delayed, or stale.
• The affected client falsely signals that “nothing happened”.

A defining diagnostic feature of BF-8 is that another client, attached to the same logical session, can often observe the completed output immediately. This confirms that BF-8 is a post-inference, client-local failure, not a reasoning or system failure.

Relationship to BF-7

BF-7 concerns attachment lifecycle and artefact expiry failures, particularly where uploaded or generated artefacts become silently unavailable or inaccessible.

BF-8 differs in that:

• Artefacts and outputs exist.
• Inference completes.
• Failure occurs at the rendering and presentation layer rather than the attachment lifecycle.

However, BF-7 and BF-8 can compound. An operator experiencing BF-8 may incorrectly attribute missing output to BF-7-style expiry, when in fact the artefact exists but is not being rendered. This interaction increases cognitive load and diagnostic ambiguity, especially under fatigue.

BF-9: Observational Asymmetry as Diagnostic Signal

BF-9 captures a higher-order Boundary Friction phenomenon identified through explicit comparison of BF-8 observations.

BF-9 occurs when multiple observers attached to the same logical session experience radically different affordances:

• One observer is trapped inside a degraded UI state (e.g. BF-8).
• Another observer, using a different client or device, can immediately observe completed outputs.
• The session itself remains coherent and continuous.
• The asymmetry between observers becomes diagnostic evidence.

BF-9 was initially parked as a provisional classification and later promoted after structured comparison demonstrated that the asymmetry was stable, repeatable, and non-accidental.

BF-9 is not a new failure of the model or system under study. It is a failure of the interaction substrate to present a coherent epistemic surface to all observers simultaneously.

In effect, BF-9 describes the friction introduced when truth exists, but is unevenly accessible.

Why This Matters

By capturing Boundary Friction instances as stratified TOML blocks and explicitly comparing them:

• Divergence could be ruled out without guesswork.
• Perspective differences could be recognised as signal rather than inconsistency.
• BF-8 could be asserted with higher confidence.
• BF-9 could be identified and named without inventing facts.

This demonstrates a broader methodological point:

In probabilistic, UI-mediated systems, comparison itself is an epistemic operation, and structured artefact capture enables that operation to be performed safely.

BF-8 and BF-9 Capture Conclusion

The deliberate capture of Boundary Friction observations as stratified TOML blocks, followed by explicit in-session comparison, transformed anecdotal UI frustration into durable engineering knowledge.

• BF-7 documents artefact lifecycle failure.
• BF-8 documents post-inference rendering-layer failure.
• BF-9 documents observational asymmetry as a diagnostic signal.

This progression reflects disciplined capture, controlled comparison, and refusal to collapse uncertainty prematurely.

BF-10 — False Liveness via Implied Asynchrony

Status

• Status: Binding (Normative)
• Classification: UI–Model Boundary Failure
• Governance: Cognitive Memoisation (CM)
• Authority: Human-curated
• Scope: Status reporting, progress signalling, interaction semantics

Requirements

1. Status reporting in synchronous systems MUST describe completed state only.
2. Terms that encode temporal continuation (e.g., “still working”, “running”, “in progress”) MUST NOT be used unless genuine asynchronous/background execution exists and is observable.
3. Where uncertainty exists, the system MUST state what is complete and what is not available, without implying ongoing work.

Rationale

BF-10 is not primarily a reasoning defect. It is a semantics defect at the boundary between system execution model and user interpretation. Eliminating false liveness restores alignment between user expectation and actual system behaviour.

Notes

• This failure mode is resolved by interface discipline, not by improved inference quality.
• Documenting BF-10 as a first-class taxonomy entry supports repeatable diagnosis and prevents reintroduction of false temporal semantics.

Why This Matters

By capturing BF instances as separate TOML blocks and explicitly comparing them:

• Divergence could be ruled out without guesswork.
• Perspective differences could be recognised as signal, not inconsistency.
• A stronger classification (BF-8) could be asserted.
• A new class (BF-9) could be identified without inventing facts.

This demonstrates a broader methodological point:

In probabilistic, UI-mediated systems, comparison itself is an epistemic operation, and structured artefact capture enables that operation to be performed safely.

Conclusion

The deliberate capture of Boundary Friction observations as stratified TOML blocks, followed by explicit in-session comparison, transformed anecdotal UI frustration into durable engineering knowledge.

• BF-8 was strengthened.
• BF-9 was discovered.
• The method itself validated the CM principle: externalise structure early so reasoning can proceed without re-derivation.

This was not accidental discovery. It was the result of disciplined capture, controlled comparison, and refusal to collapse uncertainty prematurely.

Normative Implication

Any system that does not expose an observable, durable artefact acceptance signal sufficient to re-type the execution context after isolation actions is incompatible with governed round-trip knowledge engineering under Cognitive Memoisation.

Significance for Round-Trip Knowledge Engineering

• CM enabled recognition of both friction and revelation.
• MWDUMP allowed extraction of insight from a compromised session.
• Knowledge was preserved across sessions despite UI degradation.
• Boundary effects themselves became governed artefacts.

This confirms CM as a **round-trip knowledge-engineering pattern**, not merely a documentation technique.

Boundary Revelation Class (Distinct from Friction)

paper subsumes earlier Cognitive Memoisation (CM) publications by moving from method exposition to analysis of the boundary conditions revealed when CM is applied under sustained, high-alignment human–LLM collaboration. Prior works establish CM as a pattern for progress across stateless sessions; the present work classifies user–interface boundary behaviour as a first-class epistemic constraint and occasional source of boundary revelation. Earlier claims are incorporated as methodological foundations.

BR-1: Parallel Inference Stream Revelation

Classification: Boundary Revelation (not Boundary Friction)

This phenomenon did not impede reasoning directly, but exposed latent system behaviour normally hidden by the UI.

Episodic Capture: Parallel Inference Revelation

Trigger

UI stall detected → human refreshes browser tab.

Observation

Two distinct response panels rendered concurrently.

• Both were grounded in identical conversational history.
• Both addressed the same conceptual question.
• Both were logically coherent and internally consistent.
• Inferential structure, rhetorical emphasis, and ordering diverged immediately and non-subtly.

Inference

• LLM inference is inherently stochastic.
• Multiple valid reasoning trajectories may exist for the same grounding.
• The UI normally collapses this multiplicity into a single canonical output.
• Boundary failure exposed parallel inference streams rather than causing inference error.

Human Response

• No confusion or distress.
• Immediate recognition via pattern mismatch.
• Intellectual curiosity and amusement.
• Increased clarity regarding non-deterministic inference.
• Reinforced trust in reasoning correctness, despite multiplicity.

Interpretation

This event did **not** constitute a governance failure of inference.

Instead, it demonstrated that:

• trust erosion arises when stochasticity is unmanaged or unexplained,
• not when stochasticity is visible but interpretable.

The human did not need to be “more assertive”; the model was behaving correctly. The UI simply failed to mediate coherence during recovery.

Reproducibility Notes

While internal system mechanisms are opaque, the following conditions appear contributory:

• high artefact volume,
• extended session duration,
• UI stall followed by manual refresh,
• concurrent backend inference resolution.

Exact reproduction cannot be guaranteed, but the phenomenon is observable under similar degraded conditions.

Postulate: Client-Scoped Inference Streams Within a Logical Session

Observations associated with BF-8, BF-9, and BR-1 suggest that what the user interface presents as a single conversational “session” does not, in practice, guarantee a single, unified inference stream as experienced by all attached clients.

When multiple clients (e.g. different browsers or devices) are attached to the same logical session identifier, inference completion, rendering, and response surfacing may occur independently per client. In degraded states, this independence becomes observable: one client may stall, freeze, or imply absence of progress, while another client attached to the same session can immediately observe completed inference output.

Importantly, this behaviour does not require any assumption about backend architecture. It is sufficient to observe that:

• identical conversational grounding can yield concurrently surfaced but distinct outputs,
• output availability is not globally synchronised across clients,
• and UI affordances may mask this divergence under normal operation.

The epistemic consequence is that the session abstraction is weaker than commonly assumed. It behaves as a shared reference point for conversational grounding, but not as a guarantee of shared temporal experience or output visibility.

This explains why BF-9 observational asymmetry is diagnostic rather than anomalous, and why BF-10 false liveness can arise: a client may truthfully reflect its own stalled state while another client has already progressed.

From a round-trip knowledge-engineering perspective, this implies that:

• sessions must not be treated as single-stream execution contexts,
• client-local observation is insufficient to infer global completion,
• and authoritative artefacts (e.g. MWDUMP) must remain the sole basis for asserting durable cognitive state.

This postulate reframes apparent UI inconsistency not as contradiction, but as evidence that inference, rendering, and presentation are client-scoped phenomena layered atop a shared but weakly synchronised session identity.

Conclusion

When reasoning quality is high and alignment is strong, UI boundary behaviour becomes the dominant determinant of cognitive efficiency. Some boundary failures impede progress; others unexpectedly illuminate the nature of stochastic inference. Cognitive Memoisation provides a resilient framework for preserving insight across both.

Extended human–LLM sessions under high artefact load can induce UI instability that degrades interaction quality without reflecting any failure in reasoning or governance.

Relation to Other CM Papers

This paper complements and extends:

• Holland, Ralph B. (2025). Cognitive Memoisation and LLMs: A Method for Exploratory Modelling Before Formalisation. Arising Technology Systems Pty Ltd. Version 1.3 (final). Publication date: 18 December 2025. Provenance: Authored paper maintained as a MediaWiki document; edit history reflects editorial changes, not collaborative authorship.

• Holland, Ralph B. (2025). Progress Without Memory: Cognitive Memoisation as a Knowledge-Engineering Pattern for Stateless LLM Interaction. Arising Technology Systems Pty Ltd. Version 0.4.0 (pre-release draft). Publication date: 18 December 2025. Provenance: Authored paper maintained as a MediaWiki document; clarified MWDUMP as the authoritative, permission-granting artefact governing allowable reasoning across sessions.

• Holland, Ralph B. (2025). Cognitive Memoisation: Plain-Language Summary (For Non-Technical Readers). Arising Technology Systems Pty Ltd. Companion explanatory paper. Publication date: 18 December 2025. Provenance: Authored paper intended for non-technical audiences; editorial control retained by the author.

Appendix A - Human Curation, Verification, and Provenance Integrity in Cognitive Memoisation

This appendix documents an observed human-level failure and its resolution during the preparation of this paper, and uses it to clarify recommended verification practices within the Cognitive Memoisation (CM) pattern.

N.1 Human Curation and Tooling Context

The paper was authored and curated by a human using iterative interaction with a Large Language Model (ChatGPT) as an intellectual aid. The LLM was used to assist with drafting, refinement, and structural reasoning; however, epistemic authority and editorial control remained entirely with the human author.

All authoritative content was maintained in an external system with full provenance (MediaWiki). The LLM was not treated as a memory store, but as a reasoning and pattern-matching engine operating under explicit human direction and governance.

N.2 Observed Human Failure Mode

During a late editing session, an authoritative governance artefact (MWDUMP) was inadvertently pasted as a top-level replacement for the paper’s front matter. MWDUMP is a governed extract intended to capture invariants and cognitive state; it is not a complete presentation artefact.

This action resulted in the apparent disappearance of the abstract. Importantly, no content was deleted at the storage layer; rather, a subset artefact was mistakenly substituted for a superset document due to human fatigue.

This incident constitutes a human procedural error, not a failure of Cognitive Memoisation, MWDUMP, the LLM, or the storage system.

N.3 Verification and Recovery via Provenance Analysis

The anomaly was detected through a deliberate verification step consistent with engineering practice. Instead of relying on recollection or assumption, the human author explicitly analysed the MediaWiki document provenance and revision history.

Because MediaWiki maintains immutable revision records with full text snapshots, it was possible to:

confirm that the abstract existed in prior revisions,

identify the precise revision in which it was overwritten,

determine that no semantic loss had occurred,

and restore the abstract deterministically.

The LLM was employed as an analytical aid to reason about provenance structure and failure classification, not as an authority on document state. This demonstrates the use of the LLM as a high-capacity pattern-matching and inference engine in support of human verification.

N.4 Implications for Cognitive Memoisation Practice

This episode reinforces a core design principle of Cognitive Memoisation: strongly externalised cognitive stores with intrinsic provenance are essential. CM does not depend on perfect human execution; rather, it assumes human fallibility and compensates through governance, auditability, and recoverability.

While Cognitive Memoisation names MWDUMP as its governing artefact for exporting invariant cognitive state, the pattern does not mandate a single storage technology. Other formats or systems may be employed, provided they are integrated under a comparable governing artefact that defines authority, scope, and semantics.

Without such governance, externalisation degrades into informal note-taking. With it, human–LLM collaboration becomes inspectable, reproducible, and resilient under fatigue, UI degradation, and iterative refinement.

N.5 Conclusion

The incident described here demonstrates that Cognitive Memoisation remains robust even when human error occurs. The combination of explicit governance (MWDUMP), externalised authoritative storage, and disciplined verification enables recovery without ambiguity or loss. This validates CM not only as a method for managing stateless LLM interaction, but as a general knowledge-engineering pattern that anticipates and accommodates human limitations.

Addendum: BF-7 — Live Observation and Experimental Mitigation

Status

Addendum (live, observational, non-canonical)

Context

During sustained Cognitive Memoisation (CM) work, BF-7 (UI Control-Plane Saturation / Grip) was re-encountered in real time.

This addendum records:

• new observations, and
• a candidate experimental mitigation

encountered after the original Boundary Friction paper was written.

The Boundary Friction paper was directly referenced in a contemporaneous bug report to provide grounding context. This addendum exists to preserve technical continuity between the paper and ongoing empirical work.

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Recap: BF-7 (for continuity)

BF-7 describes a UI-level degradation pattern characterised by:

• Progressive interface slowdown
• Increasing interaction latency
• Eventual UI “grip” or lock-up
• No inference error
• No user error
• No artefact error

BF-7 is a control-plane pathology, not a logic, reasoning, or CM design failure.

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New Observation (Live)

While producing large CM artefacts (e.g. Boundary Friction paper updates, MWDUMP exports, TMLDUMP exports), a previously undocumented out-of-band artefact delivery mechanism was observed:

• The system emitted a sandboxed download hyperlink for generated artefacts.
• This bypassed:
  • conversational rendering
  • copy widgets
  • clipboard containers
  • attachment ingestion paths

This behaviour was discovered accidentally and is not presented as a documented or contractual interface.

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Experimental Mitigation Hypothesis

Hypothesis: Delivering large CM artefacts via sandbox download hyperlinks reduces BF-7 severity by bypassing UI rendering and control-plane saturation.

Rationale:

• Artefacts are treated as opaque payloads rather than conversational content.
• UI DOM churn is reduced.
• Rendering and copy layers are avoided.
• Interaction collapses to a single click action.

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Early Observations (Non-Conclusive)

During limited sustained use:

• UI responsiveness subjectively improved
• BF-7 onset appeared delayed or reduced
• No negative interaction side-effects observed

These observations are environment-specific and insufficient to assert resolution.

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Scope and Caution

• This is an experiment, not a fix.
• Behaviour may vary across:
  • accounts
  • models
  • UI versions
  • regressions
• No persistence, availability, or stability guarantees are implied.
• BF-7 remains a valid and unresolved failure class.

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Explicit Exclusions

• No claims are made regarding:
  • nginx logs
  • external dataset processing
  • log-heavy analytical workflows

Such items are considered Dangling Cognates until empirically tested.

---

Next Step

• Continue short-cycle sustained testing using CM artefacts.
• Compare:
  • conversational rendering
  • attachment upload
  • sandbox download delivery
• Promote this mitigation only if repeatable benefit is observed.

Until then, this addendum remains observational and provisional.