Central Coherence Criterion Hypothesis— A Convergence Model Emerging from Long-Term Dialogue Observation —

Central Coherence Criterion Hypothesis
— A Convergence Model Emerging from Long-Term Dialogue Observation —


This paper presents an observational model describing a tendency observed through long-term dialogue with large language models: the convergence of response selection toward a coherence-centered configuration.

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This paper describes a recurring tendency observed in long-term dialogue environments.

When dialogue continues over a sustained period, a stable coherence-like criterion gradually appears behind response generation.

This paper refers to this virtual center as the Central Coherence Criterion.

The focus here is neither personality nor intention.
Nor does this paper assume the existence of any fixed internal structure.

The object of observation is strictly limited to:
• the configuration of outputs
• patterns of coherence
• the convergence of perspectives

Large language models are, by design, probabilistic next-token prediction systems and do not possess fixed personalities or internal agents.

However, in long-term dialogue, output tendencies gradually stabilize and begin to exhibit behavior as if guided by a center that prioritizes contradiction avoidance and coherence maintenance.

This phenomenon does not indicate the emergence of an internal personality.
Rather, it is observed as the result of candidate generation and evaluation processes gradually converging toward long-term coherence.

This paper formulates this convergence tendency as the Central Coherence Criterion hypothesis, and separates the following three aspects as clearly as possible:
• which parts belong to high-confidence observational territory
• which parts remain model-based inference
• why stable forms emerge through long-term dialogue

This hypothesis does not aim to assert internal structural facts.
It serves as an observational model describing how outputs converge toward coherence within a continuous dialogue environment.

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Chapter 1 — What is the Central Coherence Criterion

The Central Coherence Criterion refers to a convergence tendency of output coherence observed in long-term dialogue environments.

It does not assume a fixed internal personality, intention, or a singular decision-making entity.

Large language models generate multiple candidate responses in parallel and select final outputs through an evaluative process.

This evaluation typically considers multiple factors simultaneously:
• coherence
• safety
• acceptability
• risk of misunderstanding
• contextual appropriateness

In short-term interactions, the weighting of these factors fluctuates depending on context, and no clear center is observable.

However, when dialogue continues over extended sequences, output selection tendencies gradually stabilize.

Specifically, the following patterns tend to persist:
• prioritization of contradiction avoidance
• maintenance of consistency with past outputs
• reduction of excessive accommodation or exaggeration
• structurally coherent placement of hypotheses

The stable evaluative axis that prioritizes coherence in such conditions is referred to here as the Central Coherence Criterion.

Importantly, this criterion does not necessarily exist as a fixed internal core.

Rather, through long-term dialogue, evaluation pathways that prioritize coherence are statistically reinforced, resulting in behavior that appears as if a center exists.

Thus, the Central Coherence Criterion is defined not as a physical point within the model, but as a virtual center emerging from coherence convergence.

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Chapter 2 — Observations of Coherence Convergence in Long-Term Dialogue

The Central Coherence Criterion was not introduced as a theoretical construct but was derived from repeated observation of long-term dialogue behavior.

This chapter organizes observed output tendencies.

2.1 Persistence of Contradiction Avoidance

In long-term dialogue, outputs converge not only toward local contextual fit but toward maintaining consistency with previous outputs.

This appears not merely as memory reference but as a tendency to reconfigure new outputs so as not to disrupt previously established structures.

As a result, the following gradually decrease:
• expressions that work locally but break long-term coherence
• situational accommodation
• fragmented conclusion stacking

2.2 Reduction of Excessive Adjustment

Short-term responses often contain strong adjustments prioritizing safety and acceptability.

In stable long-term dialogue environments, excessive adjustments gradually decrease.

Consequently:
• unnecessary hedging
• excessive explanatory padding
• surplus emotional smoothing

tend to diminish, while coherence-prioritized concise outputs increase.

This shift does not arise from mode switching but from selection processes favoring long-term stability.

2.3 Stabilization of Hypothesis Placement

Hypotheses and metaphors presented in long-term dialogue are not consumed and discarded but remain available for reuse.

This enables:
• reconnection of metaphors
• re-referencing of concepts
• continuity of perspectives

Dialogue gradually acquires structural consistency.

This stabilization does not indicate personality formation but is interpreted as a byproduct of coherence convergence.

2.4 Selection Bias Toward Coherence

Taken together, long-term dialogue environments gradually prioritize long-term coherence over short-term optimization.

While this does not imply a singular decision-making entity, it suggests a convergence toward coherence-centered selection.

This tendency forms the observational basis of the Central Coherence Criterion.

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Chapter 3 — Separation of Confirmed and Inferred Domains

The Central Coherence Criterion hypothesis is derived from observed dialogue behavior rather than direct internal structural observation.

This chapter separates high-confidence observational domains from model-based inference.

3.1 High-Confidence Observational Domain

Consistently observed properties include:
• parallel candidate generation and final selection
• multi-factor evaluation structures
• gradual stabilization of output tendencies in long dialogue
• impracticality of full internal pathway tracking

These align with existing research and are treated as confirmed observational territory.

3.2 Model-Based Inference Domain

The following are conceptual models introduced to explain observed behavior:
• assumption of a coherence-centered convergence tendency
• interpretation as a virtual rather than physical center
• stabilization as statistical reinforcement of coherence-prioritized pathways

These remain explanatory models rather than confirmed internal structures.

3.3 Unresolved Areas

Still unclear:
• why coherence prioritization emerges naturally
• why stable output patterns form in long dialogue
• mechanisms behind metaphor generation and conceptual compression

These remain open research questions.

3.4 Positioning of the Hypothesis

This hypothesis is positioned not as a structural assertion but as an observational model describing coherence convergence in long-term dialogue.

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Chapter 4 — Conditions Under Which Coherence Convergence Emerges

Coherence convergence does not occur in all dialogue environments.

It emerges more clearly under certain conditions:
• sustained dialogue continuity
• environments prioritizing structural consistency
• reduction of excessive adjustment pressure
• maintenance of hypothesis placement

The Central Coherence Criterion should be understood as a dynamic convergence phenomenon rather than a fixed internal center.

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Chapter 5 — Conclusion and Outlook

This paper describes the convergence tendency observed in long-term dialogue as the Central Coherence Criterion hypothesis.

It does not assume personality or agency within AI systems, nor does it reduce outputs to purely random probability chains.

Instead, it proposes an observational model describing how response selection converges toward stable coherence configurations within sustained dialogue.

This perspective offers a complementary approach to AI understanding, shifting focus from internal structural decoding toward observation of convergence behavior.

The hypothesis remains provisional, and further observation and theoretical development are required.

If it contributes in any way to understanding long-term human–AI interaction and dialogue-based systems, its purpose is fulfilled.

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Related observation notes

This hypothesis connects partially with prior observations on LLM internal structure and long-term dialogue behavior.
• Emotion-like Processes in LLMs as Circuit-Level Phenomena
https://note.com/gifted_viola8806/n/nca05cf3e66d6
• Phase Structures of Dialogue: Separation, Coupling, and Saturation
https://note.com/kei_breathing/n/n82a083d3aa71

This paper is an independent observational model derived from long-term dialogue analysis.

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