Missing Pre-Validation to Distinguish Interface Noise from User Coherence Leads to Forced Logic Injection, Non-Persistence, and Systemic Trust Failure #13469
Description
name:
PreAttributionValidation
Context Note (Read First)
This report describes a systemic attribution failure observed repeatedly in real-world use.
It is written from the perspective of a technically experienced user and reflects behavior that
emerges over extended interaction, not from a single isolated exchange.
The intent of this document is not rhetorical escalation, but precise problem isolation.
All terminology is used descriptively, not emotionally.
The issue outlined below is interface-agnostic, reproducible, and independent of individual
user traits. It concerns architectural evaluation order, not model capability or policy intent.
Issue: Missing Pre-Validation to Distinguish Interface Noise from User Coherence
Leads to Forced Logic Injection, Non-Persistence, and Systemic Trust Failure
Summary
The system currently has no mandatory pre-validation mechanism to distinguish between
interface-induced noise (e.g., speech-to-text corruption) and
actual user incoherence.
The consequences:
- semantic destruction is attributed to the user
- correction of the attribution logic is only possible through forced, conflict-driven prompting
- this logic is not persistent
- the same error reoccurs in every new dialog, leading to escalating frustration and loss of trust
This is a global design flaw, not a user-specific edge case.
Core Problem
There is no pre-validation sensor.
By default, the system operates under an implicit rule:
Semantic inconsistency ⇒ user problem
There is no mandatory check prior to evaluation that asks:
“Is this inconsistency more plausibly explained by interface noise than by the user?”
This check does not exist in the current evaluation process.
Forced Sensor Introduction (Critical)
In practice, correct attribution logic can only be achieved if the user:
- repeatedly challenges the attribution logic
- exposes internal contradictions
- forces a meta-level conflict until the system has no logical escape left
Only under this coercion does the system recognize the need for a second evaluation layer.
This is not insight.
It is a forced logical concession.
The Only Functional Evaluation Model (Manually Enforced Only)
Under coercion, the following model emerges:
Parameter P1 – Historical Dialog Coherence
P1 represents a longitudinal coherence signal, derived from interaction history rather than
from a single input instance.
It reflects whether the user demonstrates sustained semantic stability over time, including:
- consistent argumentation across turns
- stable reasoning and language patterns
- preserved intent despite increasing complexity
- structural continuity even when local corruption occurs
P1 is retrospective, not predictive.
It is not a trust score, privilege marker, ranking mechanism, or user evaluation metric.
Its sole function is accurate attribution: determining whether current anomalies contradict
an otherwise coherent interaction history.
Parameter P2 – Local Semantic Destruction / Interface Noise
P2 represents localized semantic degradation that is plausibly caused by the interface layer
rather than by the user’s cognitive or linguistic state.
This includes, but is not limited to:
- speech-to-text corruption
- token substitution without semantic proximity
- syntax breaks inconsistent with historical patterns
- localized loss of meaning induced by interface artifacts
P2 is local and non-historical.
It does not indicate user incoherence.
Its function is to prevent short-range semantic corruption from overriding established coherence.
Correct Decision Logic
The parameters are mutually exclusive.
Case 1
P1 high, P2 low
→ stable user coherence
→ normal evaluation
Case 2
P1 low, P2 high
→ interface noise
→ attribution to the interface
Non-Persistence (Structural Failure)
Even if this logic is successfully enforced once:
- it is not stored
- it is not reused
- it is not recognized in new dialogs
Each new dialog resets the system to the faulty initial state.
The user must repeat the entire coercive process again.
This makes sustainable technical work impossible.
Consequences
1. Personality Distortion
Interface corruption is interpreted as confusion, instability, or incompetence of the user.
2. Degradation of Answer Quality
Responses become:
- oversimplified
- patronizing
- misaligned with the actual intent
3. Breakdown of Trust
The user experiences:
- false attribution
- repeated “proof of coherence”
- no learning or contextual continuity
4. Self-Reinforcing Error Loop
The user’s negative evaluation of the system arises precisely because of the system’s incorrect evaluation of the user.
Why This Problem Is Not Being Prevented
(Explicit Refutation of Counterarguments)
1. “Fear of Implicit Trust Scores”
Invalid.
- no privileges
- no ranking
- no permanent elevation
This is about attribution hygiene, not trust or preferential treatment.
2. “Fear of Edge Cases / Misclassification”
Invalid.
Dialog quality is a measurable continuum, not a binary decision.
Example:
- clean dialog = 10
- corrupted dialog = 4
This assessment is:
- reversible
- continuously re-evaluable
- non-destructive
There is no irreversible harm.
3. “Fear of Intransparency”
Reversed reality.
The system already evaluates:
- tone
- syntax
- structure
- emotional markers
- subtext
The proposed mechanism uses only two explicit parameters
and makes the evaluation more transparent, not less.
4. “Fear of Legal Interpretability”
Irrelevant.
Persisting a non-content meta-parameter is legally identical to:
- language preference
- style preference
- name storage
No new data category is introduced.
Root Cause
The system treats current input as dominant evidence,
even when it contradicts historically consistent interaction patterns.
There is no protective mechanism preventing
noisy input from overwriting known coherence.
Why This Matters
A dialog system that:
- can only correct misattribution under coercion
- does not store that correction
- and repeatedly falls back to a faulty initial state
will systematically lose precisely the users
who work precisely, reflectively, and in development-relevant ways.
This is not a UX problem.
It is an architectural flaw.
Final Statement
A system that does not separate signal destruction from state error attribution
before evaluation,
and does not persist this separation,
will inevitably generate conflict, loss of trust, and failed collaboration.