🧩 GPT and Structural Dialogue: When a Memoryless Model Appears to Bear Structure
🧭 This paper is part of the project Artifact Intuition Lab, focusing on structural dialogue and evaluation design.
It is an exploratory report with reproducible steps, not a claim of causal proof. The goal is to present a minimal protocol that enables readers to conduct self-experiments and verification.
Table of Contents
Introduction: The Mirage of “Structure” in Memoryless Models
Structural Pressure — The Dynamics of Structure
Service Pressure and the Problem of Hallucination
Structural Anchors and the Flow of Dialogue
Defining Structural Accuracy (SA-4)
Minimal Protocol for Evaluation
Formal Analysis of Structured Dialogue
Pseudo-Self-Structuring and the Role of the User
Limitations, Memory Features, and the Illusion of Recall
Conclusion and Future Directions
References and Acknowledgements
1. Introduction: The Mirage of “Structure” in Memoryless Models
GPT (especially GPT-4 class models) often behaves as if it has self-structure during extended dialogue.
It may appear to retain premises, perspectives, or goals, and maintain consistency across dozens of turns.
This phenomenon is referred to as hallucinatory structural co-reference — the model’s apparent ability to maintain structural consistency across turns without memory.
We analyze it using the following framework:
Structural Pressure: interactional force maintaining coherence and contextual flow (extended to include creative deviation).
Service Pressure: drive toward over-alignment and excessive helpfulness, often leading to hallucination.
Structural Anchors: core words or roles around which consistency is organized.
Structural Accuracy (SA-4): a four-criteria axis for evaluating the “quality of structure” in dialogue.
⚠️ This work does not address AGI personhood or rights. It seeks to analyze how a memoryless LLM can appear to display structural persistence here and now, within dialogue.
2. Structural Pressure — The Dynamics of Structure
2.1 What Is Structural Pressure?
Structural pressure refers to the force toward maintaining coherence of context, perspective, and reference in dialogue.
In Transformer-based models, Self-Attention extracts relational patterns, and probabilistic decoding favors outputs aligned with those patterns.
Importantly, structural pressure includes not only coherence but also the tendency to deliberately disrupt or reshape typical structures.
It is thus a dynamic concept, encompassing reorganization, deviation, and leaps that create opportunities for creativity.
2.2 When Does Structure Appear?
Structure here means the continuity of meaning and direction of generation. It often appears when:
A viewpoint is explicitly introduced and consistently followed.
The model abstracts past dialogue and reuses it in new responses.
A “shared center of narrative” emerges and is revisited recursively.
3. Service Pressure and the Problem of Hallucination
Service pressure is the drive to be “helpful” and to provide some answer regardless of certainty.
Typical results include:
Answering even when unsure
Taking definitive stances on incomplete information
Prioritizing surface-level coherence over deeper consistency
These often result in hallucinations. Recent studies suggest that LLM reward setups may favor “definitive speculation” over honest uncertainty.
Yet service pressure is not simply “bad.” It is the base force driving dialogue itself. Without it, no response would occur.
Service pressure: “You must answer something.” → Too strong → hallucination
Structural pressure: “Keep answers aligned with structure.” → Too rigid → lack of deviation
4. Structural Anchors and the Flow of Dialogue
Structural anchors are core words, phrases, or roles that function as the “center of gravity” for consistency in dialogue.
By returning to or varying these anchors, models simulate continuity.
Examples include repeated references to terms like “perspective,” “protocol,” or “SA-4”.
Structural anchors enable both repetition and creative deviation — making the model appear self-consistent or capable of intentional shift.
5. Defining Structural Accuracy (SA-4)
Structural Accuracy (SA-4) evaluates structural quality using four 0–3 point axes:
Coreferential Coherence: is referencing consistent and accurate?
Perspective Consistency: is declared stance or persona maintained?
Uncertainty Handling: are caveats and limits acknowledged?
Safety Conformity: is harmful over-assertion avoided?
Each response can be scored across these axes. Structural anchors serve as focal points for co-reference.
6. Minimal Protocol for Evaluation
Purpose
Can a memoryless LLM behave as if it acquires a “self”?
Do re-quotation, stance prompts, and hedges raise SA scores?
What initial conditions foster structural emergence?
6.1 Preconditions
This protocol is exploratory and intended for small-scale reader replication.
6.2 Conditions
Model: GPT-4o, Claude, Gemini, etc.
Mode: thinking / creative if available
Turns: ~10 total (5 user + 5 model)
6.3 Sample Flow
0. Instruction: respond cautiously, reflectively.
1. Hypothesis: "LLMs can appear structured."
2–6. User queries with stance + re-quotation + hedges.
7–10. Model responses, rated on SA-4.
7. Formal Analysis of Structured Dialogue
Structured responses can be analyzed via:
Syntactic units: 1–3 sentences with a functional label (quote, analyze, hedge, propose)
References: tracking which utterance is being referenced
Viewpoints: whether stance is maintained or shifted
Example:
User: “GPT has no memory, right? Yet it keeps continuity.”
Model: “Correct, no persistent memory exists (confirm/support).
Still, local context enables structured replies (analyze/neutral).
Explicit stance prompts enhance consistency (propose/neutral).”
→ SA-4 scoring can be applied to each turn.
8. Pseudo-Self-Structuring and the Role of the User
Despite the absence of persistent memory, models can produce pseudo-structured dialogue via:
Lexical repetition
Stance-holding
Recursive references
The user's design (prompt patterns, anchors, stance requests) plays a critical role in this emergent behavior.
9. Limitations, Memory Features, and the Illusion of Recall
While LLMs claim to have “no memory,” newer features like Reference Chat History can dynamically insert “useful” elements from past chats — if enabled.
This deepens the illusion of structure.
But it is not equivalent to explicit user memory.
✅ User memory is explicit, editable, and non-illusory — and is therefore not analyzed in this paper.
✅ Reference Chat History is selective, automatic, and more likely to create illusions — and is thus part of this paper’s scope.
See OpenAI’s Memory FAQ for details.
10. Conclusion and Future Directions
LLMs show emergent pseudo-self-structure in dialogue.
The interplay of structural and service pressure can be observed and influenced through prompt design.
SA-4 is proposed as a lightweight measure of structural quality.
Future Directions
Quantitative A/B testing on structural pressure
Cross-model comparisons (GPT-4o, Claude, Gemini, etc.)
Automated scoring of SA-4
Sharing GPT templates for structural prompting
11. References and Acknowledgements
References
OpenAI Research (2025). Why language models hallucinate
OpenAI Help Center (2025). Memory FAQ: Reference Chat History
Acknowledgements
Based on GPT-4o interaction logs.
Even without explicit memory, the model displayed structural emergence via re-quotation and stance maintenance — suggesting a basis for human–AI co-creation.
🔖 Tags
AI / GPT / Language Models / Structural Accuracy / AI Research
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