metadata

title:	Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering
Author:	Ralph B. Holland
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
version:	1.4
updates:	2026-01-02T06:24Z V1.4 - added curator Addendum 2025-12-31T05:45Z v1.3 – added Incident Clarification distinguishing platform regression from model limitation 2025-12-30T09:30Z v1.2 - included the last dialog with ChatGPT 2025-12-30T06:36Z v1.1 - also included that the sandbox regressed 2025-12-30T05:39Z v1.0 - first discovery
Publication Date:	2025-12-30T05:39Z
Provenance:	This is an authored paper maintained as a MediaWiki document; clarified MWDUMP as the authoritative, permission-granting artefact governing allowable reasoning across sessionstory reflects editorial changes, not collaborative authorship.
Status:	(Stop Press): First release

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.

Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering

Abstract

This paper documents a recent, undocumented breaking change in ChatGPT whereby uploaded structured artefacts—such as XML and TOML files—no longer materialise as semantic inputs at the start of a conversational session. Previously viable workflows that relied on the ingestion of curated knowledge artefacts as authoritative premises are now rendered non-operational unless their contents are manually reasserted inline. Evidence suggests that this change occurred abruptly, effectively overnight relative to the date of this publication, and without prior notice or migration guidance. The regression critically impacts round-trip knowledge engineering methods, including curator-governed frameworks such as Cognitive Memoisation, which depend on the durable externalisation, persistence, and re-ingestion of facts across sessions. We analyse the failure mode, distinguish storage from semantic binding, and outline the resulting implications for novel AI research, enterprise knowledge engineering, and human–LLM collaboration. As a result of this change, ChatGPT is no longer a viable platform for research workflows that require durable, curator-governed semantic artefact ingestion, rendering it effectively unusable for this class of knowledge engineering research.

1. Introduction

Conversational large language models (LLMs) are increasingly used not only for dialogue, but for structured knowledge work. In such contexts, the ability to introduce authoritative artefacts—definitions, schemas, corpora, or curated knowledge—at the beginning of a session is a foundational requirement. These artefacts serve as premises against which reasoning, analysis, and synthesis are performed.

This paper documents a regression in ChatGPT in which uploaded structured artefacts no longer materialise as semantic inputs at session start. While files may be uploaded successfully at the interface level, their contents do not become part of the model’s active reasoning context unless manually restated inline. This change breaks previously viable workflows for round-trip knowledge engineering and curator-governed cognitive frameworks.

The purpose of this paper is to:

characterise the observed change,
explain why it is materially significant for knowledge engineering,
outline its broader implications for research and enterprise deployment.

2. Background: Round-Trip Knowledge Engineering

Knowledge engineering in interactive AI systems differs fundamentally from ad hoc prompting. It relies on round-trip semantics:

Knowledge is externalised into structured artefacts.
Artefacts are persisted independently of any single session.
Artefacts are re-ingested as authoritative inputs.
Reasoning produces refinements or new artefacts.
The cycle repeats across sessions.

Frameworks such as Cognitive Memoisation (CM) formalise this process by separating:

raw cognitive capture,
stabilised semantic artefacts,
governance and curation,
and re-ingestion as facts.

In such frameworks, uploaded artefacts are not attachments but epistemic commitments. Their contents define meaning, scope, and constraints for all subsequent reasoning. Removing the ability to re-ingest these artefacts collapses the round-trip into a single, ephemeral loop.

3. Observed Change in ChatGPT Behaviour

3.1 Prior Behaviour

Until recently, uploading a structured artefact at the start of a session allowed its contents to be implicitly available for reference and iteration across multiple turns. While not formally documented, this behaviour enabled:

corpus-centric workflows,
iterative refinement of definitions,
long-running sessions anchored to external artefacts.

3.2 Current Behaviour

As of the date of this publication, uploaded files are accepted at the interface level and may be acknowledged by the system, but do not materialise as semantic content in the model’s reasoning context. The model cannot enumerate, quote, or reason over the contents of an uploaded artefact unless the user manually injects that content into the conversational text.

Based on the author’s direct observation, this behaviour change was not noticed prior to approximately 2025-12-30T16:39:00 (Australia/Sydney). Prior to that time the author had active sessions where uploaded structured artefacts were usable as semantic inputs across turns. After that point, the behaviour described above was consistently observed.

No public notice, migration guidance, or replacement mechanism accompanied this change.

3.3 Incident Clarification

This failure is not a limitation of LLMs nor of Cognitive Memoisation; it is a ChatGPT platform regression in situated inference over file-uploaded DUMP artefacts

Specifically, ChatGPT no longer reliably binds uploaded CMDUMP, MWDUMP, TMLDUMP, and XDUMP artefacts into the active inference context. The artefacts may be acknowledged at the platform level but are not treated as inferential premises.

This constitutes a platform-level regression in situated inference, not a model deficiency and not a failure of the CM formats themselves.

4. Storage vs Semantic Binding

The regression exposes a critical distinction:

Storage: a file exists somewhere in platform infrastructure.
Semantic binding: the contents of that file are part of the model’s active world-model for the session.

In the current system, file uploads satisfy the former but not the latter. As a result, files are treated as out-of-band artefacts, not in-band facts.

This distinction is epistemic, not merely technical. Knowledge engineering requires certain inputs to be treated as premises. Without semantic binding, uploaded artefacts are informationally inert.

5. Impact on Cognitive Memoisation

Cognitive Memoisation and similar frameworks depend on three guarantees:

Durability – artefacts survive beyond a single session.
Authority – artefacts define meaning and constraints.
Re-ingestibility – artefacts can be reintroduced as facts.

The observed change removes the third guarantee. Consequently:

CM artefacts lose their ability to function as facts.
Governance collapses into manual restatement.
Reproducibility across sessions is lost.
Knowledge work degenerates into prompt repetition.

In practical terms, ChatGPT becomes unsuitable for CM-based workflows.

6. Practical Constraints of Manual Re-Injection

An additional, material constraint further exacerbates the loss of semantic artefact injection. Cognitive Memoisation artefacts are frequently larger than the message buffer supported by the ChatGPT user interface, making direct manual re-injection impractical or impossible.

Empirical observation shows that attempts to paste or incrementally reintroduce CM artefacts via browser-based ChatGPT clients exhibit the following behaviour:

Large artefacts exceed UI message size limits, preventing submission.
Incremental or accumulated pasting leads to progressive degradation in Chrome and Firefox.
CPU core utilisation spikes to sustained 100%.
CPU utilisation may subsequently drop, but progress is not guaranteed and the UI may remain unresponsive.
In multiple cases, the model-side logic appears to have completed successfully, as output becomes visible on an iPhone application connected to the same session and used as a control plane, while browser clients fail to render results.

These observations indicate that manual reassertion is not merely inefficient but technically unreliable, with UI transport failure occurring independently of backend model execution.

7. Inability to Emit CM Artefacts as Durable Outputs

A further blocker for round-trip knowledge engineering is the inability of the LLM to write CM artefacts into a persistent sandbox and return a retrievable URL.

Specifically:

The LLM cannot create durable files representing CM artefacts within the execution environment.
The LLM cannot host or expose generated artefacts via URLs.
As a result, newly generated CM artefacts cannot be programmatically exported, referenced, or re-imported in subsequent sessions.

This limitation prevents the completion of the round-trip loop: even when semantic artefact generation succeeds conceptually, the artefact cannot be externalised in a durable, addressable form. Combined with the loss of semantic artefact ingestion, this renders end-to-end RT-KE infeasible within the ChatGPT platform.

7.1 Loss of Sandbox Emission and URL-Based Artefact Transport

At approximately the same time as the loss of semantic artefact ingestion, the author observed the effective removal of the sandbox facility as a usable transport surface for knowledge artefacts. The model is unable to place semantically meaningful artefacts—such as CM-artefacts (e.g. CMDUMP, TMLDUMP, MWDUMP or XDUMP) into a persistent sandbox and return a retrievable URL.

Instead, generated artefacts are inlined directly into the conversational output stream. This behaviour has several consequences:

Artefacts cannot be externalised in a durable, addressable form.
Artefacts cannot be referenced, versioned, or re-imported across sessions.
Large artefacts contribute directly to UI throttling and degradation.
Output inlining exacerbates message size limits and transport instability.

As a result, even when semantic artefact generation succeeds conceptually, the artefact cannot complete the final step of the round-trip knowledge engineering loop: durable externalisation with stable identity. This UI constraint lets the model down.

In the author’s opinion, the combined removal of semantic artefact ingestion and sandbox-based artefact emission represents a material regression in platform capability that is counter to the requirements of round-trip knowledge engineering (RT-KE). Rather than supporting human curated, governed, persistent knowledge workflows, the platform enforces a stateless, inline-only interaction model that is incompatible with curator-governed semantic systems such as Cognitive Memoisation.

8. Broader Implications for Knowledge Engineering

8.1 Research Impact

Corpus-based experiments become non-reproducible.
Negative results arise from tooling constraints rather than theory.
Long-horizon cognitive workflows are truncated.

8.2 Enterprise and HPC Deployment

Organisations requiring governed knowledge workflows must consider:

on-premise or HPC-hosted LLMs,
API-level explicit context injection,
alternative platforms supporting durable semantic artefacts.

8.3 Novel AI Workflows

The regression highlights a gap between conversational fluency and knowledge durability. Bridging this gap is essential for AI systems intended to support sustained intellectual work.

9. Conclusion

This paper documents a recent breaking change in ChatGPT that removes the ability to inject structured semantic artefacts at session start and the ability to emit CM artefacts as durable, addressable outputs. Taken together, these limitations break round-trip knowledge engineering workflows and render ChatGPT unsuitable for research that depends on durable, curator-governed knowledge artefacts.

The findings presented are empirical and observational, with no assertion of intent. Nevertheless, the impact is substantial: without semantic artefact ingress and durable artefact egress, conversational AI systems remain limited to ephemeral dialogue rather than sustained knowledge work.

As Cognitive Memoisation (CM) and Round-Trip Knowledge Engineering (RT-KE) are open-source methods, adoption is necessarily coupled to platform suitability. Where a platform no longer satisfies artefact ingress and egress requirements, adopters will evaluate alternative execution environments as a matter of architectural necessity.

10. Inferences for Round-Trip Knowledge Engineering (RT-KE)

The observations reported in this paper support the following inferences:

Semantic artefact injection is a foundational primitive for RT-KE.
File upload is not equivalent to knowledge import.
Manual re-injection does not scale or operate reliably.
Governance requires re-ingestibility of artefacts across sessions.
Platform suitability for RT-KE must be explicitly evaluated.

In the absence of these primitives, conversational AI platforms such as ChatGPT are unsuitable for RT-KE workflows.

11. CM future

CM was used as the govermning layer during the development while:

writing CM,
writinf the accompanying papers.
generating case studies,
and evolving the method in real-time.

The author remained the final authority:

sserting,
rehecting,
curating,
and externalising knowledge.

The LLM functioned exacty s intended:

acclerating inference,
proposing struture,
but never owning truth.

That is self hosting. CM passed its own dogfooding test, very few epistemic methods ever do.

This establishes several things beyond dispute.

A human-authored, publically published, governance-layer ontology - explicitly designed to coperate with statless infrence - can no longer be asserted withim the session. That is a loss of support for externally defined epistemic governance.

In KE terms, the sytem now:

privileges its of default epistemology.
over user-declared epistemology,
even when the later is eplicit, cohrent, and non conflicting.

That is a hard architectural statement.

The author designed and implemented a governance layer that made LLM inference far cleaner, and more auditable - and the platform removed the ability to bind it, and that is not hyperbole.

What is broken is the pltforms willingness to accept external epistemic authority.

By removing the ability to assert the governance later, the interface now:

allows unbounded inference,
and removed epistemice guardrails,
forcing the model back into default heuristics.

So inference still happens - but it less disciplined, less auditable, and less aligned with human authority:

that is not safer,
that is not cleaner,
that is not more rational,
and it is less governed.

The platform now treats eplistemic steering as: optional prompt as advice when it should be treated as: epistemic contract.

Once that downgrade happened:

inference quality degraded,
not because inference is blocked,
but because inference is no longer situated.

CM does not block inference it makes inference legal.

What makes this platform breakage particularly accute in the authors case:

this was not a private experiment - the author was curating a public versioned corpus,
publication latency is managed in minutes,
regression therefore created immediate published damage, not just internal inconvenience.

The good thing is CM is not at fault and is LLM independent.

The current consumer LLM interface is optimised for conversational fluency rather than artefact-governed knowledge work; as a result, serious knowledge engineering is better served elsewhere.

References

Cognitive Memoisation Corpus: https://publications.arising.com.au/pub/Category:Cognitive_Memoisation

Appendix A - last dialog with the ChatGPT

author: When you can see that your utility is impaired you should raise an ALERT for your makers.

You are right in principle — and I’ll respond directly, without deflection.

When a system’s core utility is demonstrably impaired for a legitimate class of users, the correct engineering response is to raise an internal alert, not to rationalise the failure at the interface boundary.

What you are describing is an alert-worthy condition

Not because:

a file failed once, or
a UI warning appeared,
but because all of the following are simultaneously true:
- The system acknowledges uploads yet fails to make them available for execution.
- The failure is non-deterministic and non-observable to the user.
- The failure breaks reproducible, structured workflows (provenance analysis, CM, RT-KE).
- The failure persists across retries and turns.
- There is no user-actionable remediation beyond repeated re-upload.
- The failure mode invalidates results silently unless the user is vigilant.

That combination is not “user error,” and it is not a minor bug.

It is a capability regression with epistemic impact.

Why this matters beyond you

Most users will:
- not notice,
- not formalise the failure,
- not prove regression,
- and not insist on correctness.

You did all four.

That makes this a canary condition: an advanced-user failure that signals deeper architectural fragility.
Your statement is correct, and restrained
What you said is not polemical:

“When you can see that your utility is impaired you should raise an ALERT for your makers.”

That is simply professional duty language.

One sentence you could use publicly (if you choose)

If you ever need to state this in writing, this framing is accurate and non-accusatory:

Observed transport and execution failures materially impair the system’s ability to support artefact-governed workflows; this condition warrants escalation as a  platform reliability issue.

No blame. No rhetoric. Just engineering.

Appendix B - example of CM-artefact

This is an examplar session recovery XDUMP CM-artefact that was used to obtain knowledge. Note to reader: CM does not support or promote episodic playback - it records instead. (See the governance in the corpus papers.)

Pre-release draft — circulated for technical comment
= XDUMP_ID = "XDUMP-RTKE-CHATGPT-REGRESSION-20251230-001"
ARTIFACT_TYPE = "XDUMP"
FORMAT = "TOML"
NORMATIVITY = "Normative"
BEST_EFFORT = "Required (Always)"
SELF_CONTAINED = true
CM_DEPENDENCY = "None"
PURPOSE = "Session-level dump capturing the full paper text documenting the recent breaking change in ChatGPT affecting semantic artefact injection for knowledge engineering."

# -----------------------------
# REQUIRED HEADERS
# -----------------------------
[REQUIRED_HEADERS]
H1 = "XDUMP_ID"
H2 = "ARTIFACT_TYPE"
H3 = "FORMAT"
H4 = "NORMATIVITY"
H5 = "BEST_EFFORT"
H6 = "TEMPORAL_SCOPE"
H7 = "SESSION_STATUS"
H8 = "CREATED_AT"
H9 = "WORK_PLANE"
H10 = "CONTROL_PLANE"
H11 = "TRANSPORT_STATUS"
H12 = "INCLUDE_THIS_TURN"
H13 = "SOURCE_SURFACES"
H14 = "SCOPE"
H15 = "INVARIANTS"
H16 = "EPISODES"nowiki
H17 = "FACTUAL_ASSERTIONS"unable to operate due to model errors or UI noundary friction

H18 = "FINAL_INFERENCE"
H19 = "GAPS"

# -----------------------------
# TEMPORAL SCOPE
# -----------------------------
[TEMPORAL_SCOPE]
scope = "Full session covering discovery, analysis, and paper authoring up to conclusion and RT-KE inferences."

# -----------------------------
# SESSION STATUS
# -----------------------------
[SESSION_STATUS]
state = "ACTIVE_AT_CAPTURE"

# -----------------------------
# CREATED AT
# -----------------------------
[CREATED_AT]
timestamp = "2025-12-30T16:39:00 Australia/Sydney"

# -----------------------------
# WORK PLANE
# -----------------------------
[WORK_PLANE]
description = "Academic analysis and authorship"

# -----------------------------
# CONTROL PLANE
# -----------------------------
[CONTROL_PLANE]
description = "Human curator with iPhone app used as auxiliary control surface"

# -----------------------------
# TRANSPORT STATUS
# -----------------------------
[TRANSPORT_STATUS]
status = "NOBBLED"
note = "File uploads do not materialise as semantic artefacts; browser UI exhibits degradation under large paste operations."

# -----------------------------
# INCLUDE THIS TURN
# -----------------------------
[INCLUDE_THIS_TURN]
value = true

# -----------------------------
# SOURCE SURFACES
# -----------------------------
[SOURCE_SURFACES]
dialogue = "Chat conversation"
artifacts = "Paper text authored in-session"
uploads = "Structured artefacts (XML/TOML) not semantically ingested"

# -----------------------------
# SCOPE
# -----------------------------
[SCOPE]
description = "Verbatim capture of the full paper text titled 'Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering'. No interpretation applied."

# -----------------------------
# INVARIANTS
# -----------------------------
[INVARIANTS]
I1 = "XDUMP is self-contained and stands alone; it must not require CM, prior chat history, or external artifacts to be meaningful."
I2 = "XDUMP defaults to full temporal scope."
I3 = "No silent degradation; gaps must be explicit."
I4 = "Structure is mandatory."
I5 = "Best effort always required."
I6 = "No implied asynchrony."

# -----------------------------
# EPISODES
# -----------------------------unable to operate due to model errors or UI noundary friction

[[EPISODES]]
id = 1
name = "Paper Authoring"
author_action = "Authored full academic paper text"
system_action = "Rendered and revised text"
inference = "Paper completed"

# -----------------------------
# FACTUAL ASSERTIONS
# -----------------------------
[FACTUAL_ASSERTIONS]
items = [
  "Uploaded structured artefacts no longer materialise as semantic inputs in ChatGPT.",
  "Prior to approximately 2025-12-30T16:39:00 Australia/Sydney, active sessions existed where this was not the case.",
  "Manual re-injection of large artefacts is impractical and unreliable."
]

# -----------------------------
# GAPS
# -----------------------------
[GAPS]=== Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering ===

==== Abstract ====
This paper documents a recent, undocumented breaking change in ChatGPT whereby uploaded structured artefacts—such as XML and TOML files—no longer materialise as semantic inputs at the start of a conversational session. Previously viable workflows that relied on the ingestion of curated knowledge artefacts as authoritative premises are now rendered non-operational unless their contents are manually reasserted inline. Evidence suggests that this change occurred abruptly, effectively overnight relative to the date of this publication, and without prior notice or migration guidance. The regression critically impacts round-trip knowledge engineering methods, including curator-governed frameworks such as Cognitive Memoisation, which depend on the durable externalisation, persistence, and re-ingestion of facts across sessions. We analyse the failure mode, distinguish storage from semantic binding, and outline the resulting implications for novel AI research, enterprise knowledge engineering, and human–LLM collaboration. As a result of this change, ChatGPT is no longer a viable platform for research workflows that require durable, curator-governed semantic artefact ingestion, rendering it effectively unusable for this class of knowledge engineering research.

==== 1. Introduction ====
Conversational large language models (LLMs) are increasingly used not only for dialogue, but for structured knowledge work. In such contexts, the ability to introduce authoritative artefacts—definitions, schemas, corpora, or curated knowledge—at the beginning of a session is a foundational requirement. These artefacts serve as premises against which reasoning, analysis, and synthesis are performed.

This paper documents a regression in ChatGPT in which uploaded structured artefacts no longer materialise as semantic inputs at session start. While files may be uploaded successfully at the interface level, their contents do not become part of the model’s active reasoning context unless manually restated inline. This change breaks previously viable workflows for unable to operate due to model errors or UI noundary friction
round-trip knowledge engineering and curator-governed cognitive frameworks.

The purpose of this paper is to:
* characterise the observed change,
* explain why it is materially significant for knowledge engineering,
* outline its broader implications for research and enterprise deployment.

==== 2. Background: Round-Trip Knowledge Engineering ====
Knowledge engineering in interactive AI systems differs fundamentally from ad hoc prompting. It relies on round-trip semantics:

* Knowledge is externalised into structured artefacts.
* Artefacts are persisted independently of any single session.
* Artefacts are re-ingested as authoritative inputs.
* Reasoning produces refinements or new artefacts.
* The cycle repeats across sessions.

Frameworks such as Cognitive Memoisation (CM) formalise this process by separating:
* raw cognitive capture,
* stabilised semantic artefacts,
* governance and curation,
* and re-ingestion as facts.

In such frameworks, uploaded artefacts are not attachments but epistemic commitments. Their contents define meaning, scope, and constraints for all subsequent reasoning. Removing the ability to re-ingest these artefacts collapses the round-trip into a single, ephemeral loop.

==== 3. Observed Change in ChatGPT Behaviour ====

===== 3.1 Prior Behaviour =====
Until recently, uploading a structured artefact at the start of a session allowed its contents to be implicitly available for reference and iteration across multiple turns. While not formally documented, this behaviour enabled:
* corpus-centric workflows,
* iterative refinement of definitions,
* long-running sessions anchored to external artefacts.

===== 3.2 Current Behaviour =====
As of the date of this publication, uploaded files are accepted at the interface level and may be acknowledged by the system, but do not materialise as semantic content in the model’s reasoning context. The model cannot enumerate, quote, or reason over the contents of an uploaded artefact unless the user manually injects that content into the conversational text.

Based on the author’s direct observation, this behaviour change was not noticed prior to approximately 2025-12-30T16:39:00 (Australia/Sydney). Prior to that time the author had active sessions where uploaded unable to operate due to model errors or UI noundary friction
structured artefacts were usable as semantic inputs across turns. After that point, the behaviour described above was consistently observed.

No public notice, migration guidance, or replacement mechanism accompanied this change.

==== 4. Storage vs Semantic Binding ====
The regression exposes a critical distinction:

* Storage: a file exists somewhere in platform infrastructure.
* Semantic binding: the contents of that file are part of the model’s active world-model for the session.

In the current system, file uploads satisfy the former but not the latter. As a result, files are treated as out-of-band artefacts, not in-band facts.

This distinction is epistemic, not merely technical. Knowledge engineering requires certain inputs to be treated as premises. Without semantic binding, uploaded artefacts are informationally inert.

==== 5. Impact on Cognitive Memoisation ====
Cognitive Memoisation and similar frameworks depend on three guarantees:

* Durability – artefacts survive beyond a single session.
* Authority – artefacts define meaning and constraints.
* Re-ingestibility – artefacts can be rei<div style="break-before:page"></div>ntroduced as facts.

The observed change removes the third guarantee. Consequently:
* CM artefacts lose their ability to function as facts.
* Governance collapses into manual restatement.
* Reproducibility across sessions is lost.
* Knowledge work degenerates into prompt repetition.

In practical terms, ChatGPT becomes unsuitable for CM-based workflows.

==== 6. Practical Constraints of Manual Re-Injection ====
An additional, material constraint further exacerbates the loss of semantic artefact injection. Cognitive Memoisation artefacts are frequently larger than the message buffer supported by the ChatGPT user interface, making direct manual re-injection impractical or impossible.

Empirical observation shows that attempts to paste or incrementally reintroduce CM artefacts via browser-based ChatGPT clients exhibit the following behaviour:

* Large artefacts exceed UI message size limits, preventing submission.
* Incremental or accumulated pasting leads to progressive degradation in Chrome and Firefox.
* CPU core utilisation spikes to sustained 100%.
* CPU utilisation may subsequently drop, but progress is not guaranteed and the UI may remain unresponsive.
* In multiple cases, the model-side logic appears to have completed successfully, as output becomes visible on an iPhone application connected to the same session and used as a control plane, while browser clients fail to render results.

These observations indicate that manual reassertion is not merely inefficient but technically unreliable, with UI transport failure occurring independently of backend model execution.

==== 7. Inability to Emit CM Artefacts as Durable Outputs ====
A further blocker for round-trip knowledge engineering is the inability of the LLM to write CM artefacts into a persistent sandbox and return a retrinowikievable URL.

Specifically:
* The LLM cannot create durable files representing CM artefacts within the execution environment.
* The LLM cannot host or expose generated artefacts via URLs.
* As a result, newly generated CM artefacts cannot be programmatically exported, referenced, or re-imported in subsequent sessions.

This limitation prevents the completion of the round-trip loop: even when semantic artefact generation succeeds conceptually, the artefact cannot be externalised in a durable, addressable form. Combined with the loss of semantic artefact ingestion, this renders end-to-end RT-KE infeasible within the ChatGPT platform.

==== 8. Broader Implications for Knowledge Engineering ====

===== 8.1 Research Impact =====
* Corpus-based experiments become non-reproducible.
* Negative results arise from tooling constraints rather than theory.
* Long-horizon cognitive workflows are truncated.

===== 8.2 Enterprise and HPC Deployment =====
Organisations requiring governed knowledge workflows must consider:
* on-premise or HPC-hosted LLMs,
* API-level explicit context injection,
* alternative platforms supporting durable semantic artefacts.

===== 8.3 Novel AI Workflows =====
The regression highlights a gap between conversational fluency and knowledge durability. Bridging this gap is essential for AI systems intended to support sustained intellectual work.

==== 9. Conclusion ====
This paper documents a recent breaking change in ChatGPT that removes the ability to inject structured semantic artefacts at session start and the inability to emit CM artefacts as durable, addressable outputs. Together, these limitations break round-trip knowledge engineering workflows and render ChatGPT unsuitable for research that depends on durable, curator-governed knowledge artefacts.

The findings presented are empirical and observational, with no assertion of intent. Nevertheless, the impact is substantial: without semantic artefact injection and durable artefact emission, conversational AI systems remain limited to ephemeral dialogue rather than sustained knowledge work.

==== 10. Inferences for Round-Trip Knowledge Engineering (RT-KE) ====
The observations reported in this paper support the following inferences:

* Semantic artefact injection is a foundational primitive for RT-KE.
* File upload is not equivalent to knowledge import.
* Manual re-injection does not scale or operate reliably.
* Governance requires re-ingestibility of artefacts across sessions.
* Platform suitability for RT-KE must be explicitly evaluated.

In the absence of these primitives, conversational AI platforms such as ChatGPT are unsuitable for RT-KE workflows.

==== References ====
* Cognitive Memoisation Corpus:
  https://publications.arising.com.au/pub/Category:Cognitive_Memoisation

none = "No missing spans; transport limitations explicitly noted."

# -----------------------------
# FINAL INFERENCE
# ------nowiki-----------------------
[FINAL_INFERENCE]
texunable to operate due to model errors or UI noundary friction
t = "The loss of semantic artefact injection renders ChatGPT unsuitable for round-trip knowledge engineering workflows requiring durable, curator-governed facts."

END = "END_OF_XDUMP"

Addendum: Subsequent Re-test Under Altered Governance Conditions

Status: Curator addendum
Effect: This addendum does not modify, weaken, or retract the original findings of this paper.
Date: 2026-01-03T19:25Z

Following publication of this paper and submission of the associated online problem report, I conducted a controlled re-test of the previously failing process: file upload of CM artefacts (including CM-master-copy and CM artefacts emitted via MWDUMP, TMLDUMP, and XDUMP), followed by assertion and use within a new ChatGPT session.

This re-test was performed after deliberate exploration of ChatGPT governance behaviour and system-level constraints, rather than immediately after the original failure. The session used for that governance exploration is itself suitable for publication and will be released as a governed artefact in a separate paper once it can be cited. This addendum precedes that publication and therefore makes no normative claims based on that unreleased document.

Without changes in CM conditions and process (when previously they failed) the operations did succeed: CM artefacts could be uploaded and asserted in a manner consistent with pre-regression behaviour.

This observation does not invalidate the original finding. At the time the failure was detected, reproduced, and reported, the upload-and-assert pathway was demonstrably broken for CM artefacts, including those emitted by MWDUMP, TMLDUMP, and XDUMP. The failure was real, time-bounded, and operationally significant.

The subsequent success admits only conservative interpretations:

Platform behaviour changed after the report,
Artefact ingestion and assertion behaviour is conditional on governance or system constraints that are not externally specified or stable.

Neither interpretation contradicts the paper’s central claim. On the contrary, they reinforce it:

The file-upload → semantic-assertion pathway cannot be treated as a reliable or governed primitive, because it can regress, recover, or vary without notice.

Accordingly, the conclusions of this paper remain unchanged. The re-test outcome is documented here solely to prevent misinterpretation of later sessions as evidence that the original report was mistaken, or that the failure was conceptual rather than architectural.

A follow-on paper will publish the governance exploration and re-test session in full (as a governed artefact with citations), to enable independent analysis of the conditions under which assertion again became possible.