Recent Breaking Change in ChatGPT: The Loss of Semantic Artefact Injection for Knowledge Engineering
= Appendix A - example of CM-artefact This is the session recovery XDUMP CM-artefact that mnay be used when CM proper is unable to operate due to model errors or UI noundary friction
= 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" H17 = "FACTUAL_ASSERTIONS" 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 # ----------------------------- 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 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.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.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 # ----------------------------- [FINAL_INFERENCE] text = "The loss of semantic artefact injection renders ChatGPT unsuitable for round-trip knowledge engineering workflows requiring durable, curator-governed facts." END = "END_OF_XDUMP" <</pre<