Dimensions of Platform Error: Epistemic Retention Failure in Conversational AI Systems
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| 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: | 1.0.0 |
| Publication Date: | 2026-01-13T23:29Z |
| Provenance: | This is an authored paper maintained as a MediaWiki document. |
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Dimensions of Platform Error: Epistemic Retention Failure in Conversational AI Systems
Abstract
When Conversational 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. They are incapable of retaining binding assertions, corrections, scope, or 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. Its 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 preserve assertions as commitments, corrections as binding, definitions as authoritative, scope as operative, and provenance as attached to meaning.
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 attributable6. Conclusion
In the authors’ opinion, the absence of epistemic retention constitutes the fundamental failure of conversational AI platforms, and no additional corrective factors are required to account for the observed breakdowns.
References
7.1 Epistemic Instability and Hallucination as Structural Phenomena
- Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025). Why Language Models Hallucinate. arXiv.
- https://arxiv.org/abs/2509.04664 :contentReference[oaicite:0]{index=0}
- OpenAI. (2025). Why language models hallucinate.
- https://openai.com/index/why-language-models-hallucinate/ :contentReference[oaicite:1]{index=1}
7.2 Failure of Binding Assertions and Non-Persistent Corrections
- Marcus, G., & Davis, E. (2020). GPT-3, bloviator: OpenAI’s language generator has no idea what it’s talking about. MIT Technology Review. :https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/
7.3 Supersession Failure (Corrections Do Not Override Prior Output)
7.4 Provenance Failure and Non-Binding Sources
- Reuters Institute for the Study of Journalism. (2025). Generative AI and news: Accuracy, attribution, and trust.
7.5 Governance, Auditability, and Accountability Failure
- Whittaker, M., Crawford, K., & Dobbe, R., et al. (2018). AI Now Report 2018. AI Now Institute.
7.6 Professional Domain Failure (Empirical Evidence)
- Reuters. (2025). AI assistants make widespread errors about the news, new research shows.
- The Verge. (2025). AI chatbots are distorting news stories, BBC finds.
7.7 Epistemic Limits of Conversational AI Architectures
- (Note: arXiv papers Calibrated Language Models Must Hallucinate
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