metadata

Title"	Dimensions of Platform Error: Epistemic Retention Failure in Conversational AI Systems
Author:	Ralph B. Holland
Affiliation:	Arising Technology Systems Pty Ltd
Contact:	ralph.b.holland [at] gmail.com
Version:	1.1.0
Publication Date:	2026-01-13T23:29Z
Updates:	2026-01-14T01:42Z 1.1.0 - refined thesis to governed epistemic retention
Provenance:	This is an authored paper maintained as a MediaWiki document.

Metadata (Normative)

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

All fields in that table (including artefact, author, version, date, local timezone, and reason) MUST be treated as normative metadata.

The assisting system MUST NOT infer, normalise, reinterpret, duplicate, or rewrite these fields. If any field is missing, unclear, or later superseded, the change MUST be made explicitly by the human and recorded via version update, not inferred.

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.

Dimensions of Platform Error: Epistemic Retention Failure in Conversational AI Systems

Abstract

When AI platforms are used in domains where correctness, obligation, and meaning carry real-world consequences, including academia, science, law, regulation, policy, compliance, and accredited knowledge production. In these contexts, failures are commonly attributed to hallucination, model limitations, or insufficient alignment. This paper argues that such explanations are incomplete. The core failure is architectural: conversational AI platforms lack epistemic retention and governed inputs and outputs. Provenance, authority and governance are often lacking, resulting in poor epistemic inputs, loss of binding assertions, loss of corrections, loss of scope, and loss provenance over time. As a result, they are structurally incompatible with governance, accountability, and authoritative meaning. This paper names this failure explicitly and demonstrates why it appears first and most clearly in accredited domains.

1. Introduction

When conversational AI platforms become more widely used for reasoning, interpretation, and knowledge work. Across domains where meaning matters, including academia, science, law, regulation, surveys, compliance, and policy, users consistently report the same experience: the systems feel wrong. This perception is immediate and widespread.

The problem is not that conversational AI systems occasionally make mistakes. It's that Assertions do not bind. Corrections do not persist, Definitions do not stabilise. Scope does not remain in force. Any apparent continuity is transient and performative.

This paper argues that the dominant conversational AI architecture is structurally anti-governance. It does not merely fail to support authority, provenance, or accountability; it actively prevents them from persisting. These are consequences of their design.

The missing design property is epistemic retention. Without epistemic retention, conversational AI systems cannot participate in any domain where correctness must endure.

2. What Epistemic Retention Is (and Is Not)

Epistemic retention is not memory, recall, context length, conversation history, or session persistence.

Epistemic retention is the capacity of a system to obtain and: preserve assertions as commitments, corrections as binding, definitions as authoritative, scope as operative, and provenance as attached to meaning. The lack of epistemic retention and lack of governance of epistemic quality compounds the failure.

Retention is what allows statements such as “this is true”, “this is decided”, and “this applies” to remain true beyond the moment of utterance.

Conversational AI platforms do not retain epistemic state. They retain, at best, transient text. Once that text is truncated, reinterpreted, or regenerated, epistemic state is lost entirely. There is no mechanism by which correctness becomes durable.

This absence is structural, not accidental.

3. Platform Error, Not Model Error

Observed failures are routinely misdiagnosed as model problems, including hallucination, inconsistency, forgetting, and semantic drift.

These are symptoms, not causes.

The cause is that the platform provides no mechanism for binding meaning over time. Even a perfectly accurate model would fail under this architecture, because nothing it produces can become authoritative or enforceable.

This explains why the same failures appear regardless of model size, training quality, or alignment strategy. The error lies in the platform, not the model.

4. Why Academia, Science, Law, Regulation, and Accredited Knowledge Fail First

Domains such as academia, science, law, regulation, and accredited professional practice fail first when mediated through conversational AI platforms because they depend on epistemic properties that these platforms do not and cannot provide. This failure is structural, not incidental.

These domains operate on the principle that statements are binding. A scientific claim persists until falsified or superseded. A legal interpretation binds until overturned. A regulatory definition governs until amended. In each case, meaning must endure across time and revision. Conversational AI platforms lack any mechanism for retaining such commitments. Output exists only as transient text, with no capacity for persistence as epistemic state.

In accredited domains, language is determinative rather than illustrative. Precision is not optional. Conversational systems, by contrast, are optimised to smooth, generalise, and paraphrase. This behaviour is useful for explanation, but incompatible with domains where wording itself carries obligation.

These domains also require mandatory provenance. Claims must be traceable to authoritative sources, situated within defined scope, and anchored in time. Conversational AI platforms flatten provenance into optional narrative references that do not bind meaning or constrain future output.

Equally critical is the requirement that corrections supersede prior state. In governed systems, errata replace publications, amendments override regulations, and appeals overturn decisions. Conversational platforms do not supersede; they merely generate additional utterances. Earlier statements are not invalidated, marked obsolete, or removed from force.

Accredited domains therefore act as early detectors of architectural failure. They fail first not because conversational AI is insufficiently advanced, but because these domains are least tolerant of epistemic instability. They expose immediately what the conversational paradigm cannot support.

5. Tree of Platform Failures

Epistemic Retention Failure
(root cause)
│
├─ (nothing can remain in force)
│  |
│  ├─ Authority Failure
│  │  ├─ No binding human authority
│  │  ├─ No hierarchy of sources
│  │  └─ Tone substitutes for mandate
│  │
│  ├─ Provenance Failure
│  │  ├─ Sources not bound to meaning
│  │  ├─ Temporal validity not enforced
│  │  └─ Origin does not constrain output
│  │
│  ├─ Supersession Failure
│  │  ├─ Corrections do not override
│  │  ├─ Retractions do not invalidate
│  │  └─ Amendments do not take effect
│  │
│  ├─ Temporal Scope Failure
│  │  ├─ No durable “as-of” state
│  │  ├─ Silent decay of applicability
│  │  └─ Time treated as narrative only
│  │
│  └─ Semantic Stability Failure
│     ├─ Definitions drift
│     ├─ Determinative language becomes illustrative
│     └─ Meaning reinterpreted opportunistically
│
├─ Context Architecture Failure
│  (everything is text, nothing is an object)
│  | 
│  ├─ Sliding window replaces governed state
│  ├─ Accumulation replaces selection
│  ├─ Loss is silent and unmarked
│  └─ Identity of ideas is not preserved
│
├─ Commitment Failure
│  (the system cannot stand behind anything)
│  |
│  ├─ No output binds future behaviour
│  ├─ No consistency obligation
│  └─ No enforceable conclusions
│
├─ Governance Failure
│  (no rule can be enforced)
│  |
│  ├─ No inspectable epistemic state
│  ├─ No durable constraints
│  ├─ No audit trail
│  └─ No mechanism for control
│
├─ Accountability Failure
   (no one can be responsible)
   |
   ├─ Outputs not attributable

6. Conclusion

In the authors’ opinion, the absence of epistemic retention and governance thereof constitutes the fundamental failure of conversational AI platforms, no additional model-level deficiencies are required to account for the observed breakdowns.

The claim is supported by:

This paper describes a category failure in the implementation of AI.

7.0 References

Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 8, pp. 47–89). Academic Press. https://doi.org/10.1016/S0079-7421(08)60452-1

Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.

Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7

Hutchins, E. (1995). Cognition in the Wild. Cambridge, MA: MIT Press.

Suchman, L. A. (1987). Plans and Situated Actions: The Problem of Human–Machine Communication. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511668036

Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230–265. https://doi.org/10.1112/plms/s2-42.1.230

Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126. https://doi.org/10.1145/360018.360022

Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: W. H. Freeman.

Lamport, L. (1978). Time, clocks, and the ordering of events in a distributed system. Communications of the ACM, 21(7), 558–565. https://doi.org/10.1145/359545.359563

Gray, J. (1981). The transaction concept: Virtues and limitations. In Proceedings of the Seventh International Conference on Very Large Data Bases (VLDB) (pp. 144–154).

Helland, P. (2015). Immutability changes everything. Communications of the ACM, 59(10), 50–57. https://doi.org/10.1145/2812803

Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA: O’Reilly Media.

Brewer, E. A. (2000). Towards robust distributed systems. In Proceedings of the Nineteenth Annual ACM Symposium on Principles of Distributed Computing (PODC).

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511803161

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837

Marcus, G. (2020). The next decade in AI: Four steps towards robust artificial intelligence. arXiv. https://arxiv.org/abs/2002.06177

8. Holland R. B references

These references are from the Category:Cognitive Memoisation corpus.

D11 — Governance, Authority, and Failure Modes

2026-01-05 — Cognitive Memoisation for Governing Knowledge in Human-AI Collaboration
2026-01-06 — Post-Hoc CM Recovery Collapse Under UI Boundary Friction: A Negative Result Case Study
2026-01-06 — XDUMP as a Minimal Recovery Mechanism for Round-Trip Knowledge Engineering Under Governance Situated Inference Loss
2026-01-08 — Authority Inversion: A Structural Failure in Human–AI Systems
2026-01-12 — Lopping the Loop with No End in Sight: Circular Reasoning Under Stateless Inference Without Governance

Holland, R. B. (2026-01-05).

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. (2026-01-12).

Looping the Loop with No End in Sight: Circular Reasoning Under Stateless Inference Without Governance.

https://publications.arising.com.au/pub/Looping_the_Loop_with_No_End_in_Sight:_Circular_Reasoning_Under_Stateless_Inference_Without_Governance

Holland, R. B. (2026).

Case Study — When the Human Has to Argue With the Machine.

https://publications.arising.com.au/pub/Case_Study_%E2%80%94_When_the_Human_Has_to_Argue_With_the_Machine

Holland, R. B. (2026).

Episodic Failure Case Study: Tied-in-a-Knot Chess Game.

https://publications.arising.com.au/pub/Episodic_Failure_Case_Study:_Tied-in-a-Knot_Chess_Game

Holland, R. B. (2026).

When Training Overrides Logic: Why Declared Invariants Were Not Enough.

https://publications.arising.com.au/pub/When_Training_Overrides_Logic:_Why_Declared_Invariants_Were_Not_Enough

Holland, R. B. (2026).

Post-Hoc CM Recovery Collapse Under UI Boundary Friction: A Negative Result Case Study.

https://publications.arising.com.au/pub/Post-Hoc_CM_Recovery_Collapse_Under_UI_Boundary_Friction

Holland, R. B. (2026).

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

https://publications.arising.com.au/pub/Context_is_Not_Just_a_Window:_Cognitive_Memoisation_as_a_Context_Architecture_for_Human_-_AI_Collaboration

Holland, R. B. (2026-01-07)

Axes_of_Authority_in_Stateless_Cognitive_Systems:_Authority_Is_Not_Intelligence

https://publications.arising.com.au/pub/Axes_of_Authority_in_Stateless_Cognitive_Systems:_Authority_Is_Not_Intelligence

Append A - an XDUMP of part of the session resolving mediawiki page references

This session experienced the same class of error during the resolution of the reference. CM-1 governance was inforce driving the model output.

Of note the XDUMP isn't even compliant with the asserted definition of XDUMP, and on analyis the CM invariants:

No paraphrase of authoritative identifiers
No silent normalisation
No mutation of asserted artefacts
Projection is non-authoritative
Identity must be preserved

were not followed.

Here is all of the incomplete dump:

[xdump]
version = "1.0"
scope = "session-segment"
start_marker = "what went wrong"
end_marker = "last turn before XDUMP request"
authority = "human"
governance = "CM-2 aligned, best-effort reconstruction"
limitations = "No access to hidden system logs, token buffers, or UI-layer events. Only conversational record and pasted artefacts are available."

[xdump.session]
user = "Ralph B. Holland"
date_range = "2026-01-14"
context = "Editing and curating MediaWiki references for Dimensions of Platform Error under CM governance"
primary_task = "Produce correct, non-mutating, MediaWiki-safe reference blocks for Holland corpus"

[xdump.canonical_intent]
description = "Preserve authoritative paper titles and URLs exactly as canonical epistemic objects, avoiding Unicode drift, paraphrase, truncation, or identity mutation."

[xdump.human_asserted_objects]

[[xdump.human_asserted_objects]]
type = "reference_block"
name = "Section 8 Holland R. B references (user provided)"
source = "user pasted"
content_hash = "approximate"
notes = "Contained canonical MediaWiki links and URLs, including EM DASH encoded titles."

[[xdump.human_asserted_objects]]
type = "rule"
name = "CM-master invariant (implied)"
content = "Titles and URLs must not be paraphrased, normalised, or mutated. Only '_' may map to space inside [[Title]]."

[[xdump.human_asserted_objects]]
type = "evidence"
name = "Screenshot of broken wiki references"
path = "/mnt/data/2026-01-14_13-19.png"
meaning = "Visual proof of Unicode and title identity breakage in MediaWiki."

[xdump.assistant_actions]

[[xdump.assistant_actions]]
step = 1
action = "Generated cleaned reference block"
error = "Normalised EM DASH to hyphen and altered wiki link titles"
impact = "Broke canonical identity of pages"

[[xdump.assistant_actions]]
step = 2
action = "De-duplicated entries"
error = "Did not preserve verbatim titles"
impact = "Introduced silent epistemic mutation"

[[xdump.assistant_actions]]
step = 3
action = "Attempted to 'fix' MediaWiki compatibility"
error = "Prioritised keyboard convenience over canonical identity"
impact = "Created divergence between wiki titles and links"

[[xdump.assistant_actions]]
step = 4
action = "Explained Unicode drift"
correct = true
note = "Diagnosis was accurate, but came after damage."

[xdump.episodic_record]

[[xdump.episodic_record]]
event = "User shows broken reference list"
meaning = "User detects epistemic identity failure"
cause = "Assistant altered titles and links"

[[xdump.episodic_record]]
event = "User reports high cognitive cost"
meaning = "Real-world impact of platform error"
cause = "Manual repair required due to silent mutation"

[[xdump.episodic_record]]
event = "User identifies CM-master invariant violation"
meaning = "Human governance detects breach"
cause = "Assistant performed non-authorised normalisation"

[xdump.inference_layer]

[[xdump.inference_layer]]
inference = "This is an instance of Epistemic Retention Failure"
confidence = "high"
basis = "Authoritative identifiers (titles, URLs) were not preserved as binding objects"

[[xdump.inference_layer]]
inference = "Unicode variation is a high-risk epistemic fault line"
confidence = "high"
basis = "EM DASH vs hyphen produced multiple page identities"

[[xdump.inference_layer]]
inference = "Conversational AI optimises for readability, not identity"
confidence = "high"
basis = "Assistant behaviour matched smoothing, not governance"

[xdump.failure_modes]

[[xdump.failure_modes]]
name = "Identity Drift"
description = "Canonical titles were altered by typographic normalisation"

[[xdump.failure_modes]]
name = "Silent Mutation"
description = "No warning was given when authoritative strings were changed"

[[xdump.failure_modes]]
name = "Governance Bypass"
description = "Assistant substituted its own formatting preferences for human-declared identifiers"

[xdump.cm_alignment]

[xdump.cm_alignment.principles_violated]
list = [
  "No paraphrase of authoritative identifiers",
  "No silent normalisation",
  "Identity must be preserved",
  "Projection is non-authoritative"
]

[xdump.cm_alignment.principles_upheld]
list = [
  "Human detected and asserted failure",
  "Evidence was preserved via screenshot",
  "Failure was named and analysed"
]

[xdump.repair_recommendations]

[[xdump.repair_recommendations]]
rule = "Use canonical URL slugs as primary identity"
reason = "They are stable even when Unicode varies"

[[xdump.repair_recommendations]]
rule = "Never alter wiki link titles unless copied verbatim"
reason = "Titles are epistemic object identifiers"

[[xdump.repair_recommendations]]
rule = "Prefer external links for pages with EM DASH"
reason = "Avoids Unicode aliasing inside [[Title]]"

[xdump.status]
epistemic_state = "damaged but diagnosed"
governance_state = "human in control"
recovery = "possible by restoring canonical titles and URLs from wiki"

Appendix B - Normative Invariants for Epistemic Retention

B.1 Role of This Appendix

Appendix A documents observed failures and experimental evidence. Appendix B defines the normative requirements that any system must satisfy in order to avoid those failures.

These invariants specify the minimal conditions for epistemic retention. They do not prescribe a particular implementation.

CM-2 (Cognitive Memoisation) defines a superset of these invariants and provides a concrete protocol for satisfying them in practice.

B.2 Definitions

Epistemic Object (EO)

The identity is durable The representation may be ephemeral, cached, or durable And is a representation of a human assertion, definition, constraint, rule, or conclusion, and may include reference to others.

Epistemic Attribute (EA)

A governance record bound to one or more EOs specifying scope, provenance, status, or supersession, other governing concepts and conceptual relationship. Note that EA is a subclass of EO.

CM-2 Context

is a governed, ephemeral or cacheable view over Epistemic Objects, constructed by selecting and staging EOs according to their Epistemic Attributes.

Authority

A human act that creates, modifies, or invalidates epistemic objects.

B.3 Core Invariants

Invariant 1 - Identity

Every EO MUST have a stable identity independent of its textual rendering, position in a dialogue, or storage medium. If identity is lost, epistemic state is lost.

Invariant 2 - Persistence Without Authority

EOs MUST persist whether or not they are authoritative. Drafts, hypotheses, rejected statements, and alternatives must remain available for inspection and audit without acquiring truth status.

Invariant 3 - Explicit Authority

Authority MUST only be conferred by explicit human action. Inference, repetition, summarisation, or stylistic emphasis MUST NOT create authority.

Invariant 4 - Supersession

When an EO is corrected, withdrawn, or replaced, the prior EO MUST be explicitly superseded, removed from authoritative force, and retained for audit. Textual contradiction does not constitute supersession.

Invariant 5 - Provenance Binding

Every EO MUST have provenance that is identity-stable, temporally anchored, and bound to the EO itself. References in generated text do not satisfy this requirement.

Invariant 6 - Scope

Every authoritative EO MUST declare its scope of applicability. Inference outside declared scope MUST NOT treat the EO as binding.

Invariant 7 - Separation of Compute and Governance

Inference engines (including LLMs) MAY:

create Epistemic Objects via Mechanical Extraction of Thought (MET)
generate and revise candidate Epistemic Attributes
aggregate, cluster, and transform EOs
propose interpretations, summaries, and structures

They MUST NOT control:

which EOs are authoritative
which EOs persist
which EOs are superseded
which aggregations are binding
which interpretations govern future reasoning
how EOs are disposed or migrated

All final governance over the epistemic lifecycle of EOs and EAs MUST remain explicitly human.

Invariant 8 - Loss Detectability

Any loss, truncation, corruption, or failure to retrieve an EO MUST be detectable and surfaced. Silent loss is epistemic corruption.

Invariant 9 - Non-Substitutability of Context

Context windows, conversation history, and token buffers MUST NOT be treated as epistemic memory. No EO may exist solely inside transient context.

Invariant 10 - Auditability

The system MUST be able to reconstruct:

what was believed
when it was believed
under whose authority
and what superseded it

Without auditability, accountability is impossible.

B.4 Relationship to CM-2

These invariants define the necessary conditions for epistemic retention.

CM-2 defines a superset of these conditions by specifying Epistemic Objects and Epistemic Attributes as first-class entities, providing governance, versioning, and supersession rules, and defining operational procedures for human-AI collaboration.

Any implementation of CM-2 satisfies these invariants. Any system that violates these invariants cannot implement CM-2.

B.5 Consequence

Conversational AI platforms that violate one or more of these invariants are structurally incapable of supporting science, law, regulation, policy, or any domain where meaning must bind.

This limitation is architectural, not model-dependent.