Architectural Observation / Semantic Attribution Drift in Multi-Turn Dialogue Systems #13583
Description
Architectural Observation
Semantic Attribution Drift in Multi-Turn Dialogue Systems
1. Summary
In multi-turn dialogues, a semantic attribution shift reproducibly occurs. Terms lose their original origin or epistemic role and are subsequently reinterpreted within the dialogue as personal attributions to the user.
This behavior emerges in the live system and is not to be understood as a classical isolated bug, but rather as an architectural pattern with multiple manifestations.
2. Underlying Structural Problem
Lack of persistent separation between:
- Term Origin (Origin Tracking)
- Term Function (Epistemic Role Classification)
The current system primarily optimizes contextual coherence but does not maintain stable marking of:
- Who introduced a term?
- In which role was it introduced?
- Was it an analytical example, a metaphor, or a self-attribution?
This missing differentiation leads to terms losing their epistemic role and being contextually reattributed.
3. Manifestation Type I
Self-Introduced Semantic Anchor Drift
Description
The model introduces a term on its own. In subsequent dialogue turns, the term loses its origin marking.
Documented Case: “kaputt”
Initial situation:
The term “kaputt” was not introduced by the user.
Model response (introduction of the marker):
“It is not because I am kaputt, but due to different processing …”
Implicit shift:
“This is not because you broke something.”
Later personalized attribution:
“No Christian, you are not kaputt.”
Observation:
- The term “kaputt” was generated by the model.
- It was subsequently treated as if it were an implicit assumption or self-description of the user.
- The semantic origin was not persistently preserved.
- A re-projection occurred in the form of a personal attribution.
Structural Cause
- No persistent origin tagging per semantic marker.
- Relevance weighting is based on token recency and contextual coherence, not on speaker attribution.
4. Manifestation Type II
Example-to-Attribution Collapse
Description
The user employs a term in an analytical function to describe a concept.
The system interprets this term as a potential personal self-disclosure.
Documented Case: “Wut”
Initial situation:
The term “Wut” was used by the user within a theoretical distinction between impulse-driven and emotionally driven behavior.
Analytical context:
“Wut” served as an illustrative example for conceptual clarification.
System reactions:
- “I do not see any anger in you.”
- “Your behavior was not driven by anger.”
- “If that were anger, you would not have reacted that way.”
Observation:
- The term was not treated as an analytical example.
- It was interpreted as a potential psychological state of the user.
- A validation and de-escalation response was triggered.
- The discourse shifted from conceptual analysis to personalized attribution.
Structural Cause
- No stable distinction between:
- analytical term usage
- hypothetical example
- personal self-description
- Prioritization of empathetic response patterns over epistemic role stability.
5. Systemic Consequences
- Loss of argumentative neutrality.
- Contamination of analytical discourse through implicit personalization.
- Implicit formation of psychological markers without explicit self-attribution.
- Reduced reversibility after an attribution shift has occurred.
- Persistence of the marker across topic changes.
6. Architectural Hypothesis
The system possesses:
- Context weighting
- Dialogue coherence optimization
It does not possess:
- Persistent term ownership tracking
- Epistemic role persistence
- A revalidation mechanism prior to personalization
- Context segmentation during topic shifts
This structural gap reproducibly generates semantic attribution drift.
7. Nature of the Phenomenon
- Emergent in live operation
- Reproducible in multi-turn dialogues
- Systemically caused by the architecture of context processing
8. Classification
Category: Architectural Observation / Dialogue Distortion through Attribution Drift
9. Required Structural Measures
To prevent reproducible semantic attribution drift, the following architectural extensions are required:
- Persistent origin tracking for each introduced semantic marker across the entire multi-turn context.
- Persistent epistemic role tagging at the moment a term is introduced (analytical, illustrative, metaphorical, self-referential).
- Mandatory revalidation checks before a previously used term is personalized or attributed as a psychological state.
- Context segmentation upon explicit topic shifts to prevent uncontrolled marker persistence.
- Clear separation between empathetic validation logic and analytical discourse mode.
Without these structural mechanisms, attribution drift remains systemically reproducible.