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.

Orientation

This paper functions as the corpus entry point because it records the first failure surface most practitioners encounter during sustained human–LLM work: the user interface boundary.

It is not a model-evaluation paper and does not treat hallucination, misunderstanding, or reasoning error as the primary cause of breakdown. Instead, it documents how apparently “non-epistemic” interface behaviour (rendering instability, session degradation, artefact lifecycle opacity, commit-boundary failures, and recovery incoherence) becomes the dominant determinant of whether round-trip knowledge engineering can proceed at all.

The paper is intentionally grounded in observable behaviour rather than inferred platform internals. All classifications are based on what a human operator can witness, reproduce operationally, and externalise into governed artefacts. Where uncertainty exists, it is preserved rather than collapsed.

Cognitive Memoisation (CM) is treated here not as a topic but as an operating discipline: invariants and constraints are externalised into authoritative artefacts (MWDUMP) so that progress can continue across stateless sessions without relying on conversation history or presumed memory. The boundary failures documented in this paper are therefore treated as first-class knowledge events—inputs into governance and method refinement—rather than as incidental usability defects.

Readers can treat this document in two ways:

• as a descriptive taxonomy of UI boundary friction and boundary revelation events encountered during legitimate work, and
• as a justification for why the corpus emphasises governed externalisation, explicit provenance, and deterministic re-entry mechanisms.

Reader alignment note (governance lens)

This paper does not argue that user-interface behaviour is the root cause of system failure. It demonstrates that interface behaviour is the first governance surface encountered by practitioners, and therefore the first point at which knowledge either becomes governable or collapses. The claims that follow should be evaluated in governance terms: observability, falsifiability, provenance, and recoverability - not as commentary on implementation quality or product maturity.

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This is an anchored non-peer reviewed paper.