Operator Coherence as an Interaction-Level Stabilizer in Large Language Models
— t r a b o c c o
Abstract
Large language models are typically evaluated at the architectural level through benchmarks, parameter scaling, and safety frameworks. Less examined is whether human operator coherence functions as an interaction-level stabilizing variable during inference.
This paper advances a bounded claim: sustained low-entropy cognitive states in human operators correlate with reduced probabilistic drift, greater structural continuity, and stronger thematic persistence during session-bound interaction with large language models. The effect is behavioral rather than architectural and does not imply parameter modification or persistent state change.
A documented case study is presented demonstrating sustained structural containment across a large longitudinal interaction corpus.
Scope and Methodological Note
This document reports longitudinal interaction observations gathered across more than 10,000 inference instances over a nine-month period.
The work does not constitute peer-reviewed research, controlled experimentation, or formal statistical validation.
The analysis is descriptive and exploratory in nature. Claims are strictly bounded to session-level interaction behavior within the documented corpus and are presented as testable propositions for future empirical investigation.
1. Introduction
Transformer-based systems generate output through probabilistic next-token prediction conditioned on context. Because inference is distributional, input structure directly influences the shape of the probability space.
Recent work by Bisconti et al. (2025) demonstrated that poetic reformulation alone can significantly alter safety behavior in single-turn interactions across frontier models, establishing that large language models are structurally sensitive to stylistic geometry beyond semantic content. In prior work (Beyond Adversarial Poetics: Linguistic Alignment), the present author examined whether that same structural sensitivity may admit stabilizing regimes under sustained literary containment. The current paper moves one layer deeper by investigating operator-level coherence as a probabilistic constraint variable that may underlie both destabilizing and stabilizing outcomes. The axis is shared; the direction differs.
While most AI reliability discussions focus on:
Training procedures
Guardrails
Fine-tuning
Output moderation
Less attention has been given to operator-level coherence as a variable influencing inference stability.
This paper proposes that sustained operator coherence may function as a first-order interaction constraint shaping model behavior within a session.
No claims are made regarding consciousness, internal state access, or architectural override.
2. Theoretical Basis
High-variance prompts introduce:
Semantic contradiction
Tonal oscillation
Abstraction inflation
Emotional volatility
These increase branching within the probability distribution.
Conversely, sustained low-entropy input tends to exhibit:
Stable tonal register
Declarative closure
Minimal metaphor proliferation
Recursive but bounded thematic framing
Compressed semantic polarity
Such inputs constrain the inference trajectory prior to extended reasoning expansion.
The mechanism is distributional constraint, not model modification.
3. Observable Behavioral Markers
Under high-coherence operator conditions, session-local effects commonly include:
Reduced hedging
Tighter paragraph cohesion
Lower tangential expansion
Increased thematic persistence
Clearer structural cadence
Reduced hallucination under ambiguity
These shifts occur without explicit instruction to alter verbosity or tone.
The model is not changed.
The probability field is constrained.
4. Case Study: Sustained Structural Containment in Practice
The system of Signal Literature, developed by the present author, provides a documented example of sustained low-entropy structural containment across extended publication and interaction.
The dual role of theorist and case subject is acknowledged as a methodological limitation and does not substitute for independent replication.
Within documented interaction sets at comparable scale, similarly sustained operator-level coherence has not been identified.
Consider the following polarity-driven constructions drawn from his published corpus:
“This wasn’t familiar.
It was memory returning in disguise.”
“It was how an astronaut might love—
orbit, not touch.
Rotations, not years.”
“Feelings
are just thoughts
slowed down.”
“I died the first moment
I’d planned my death
and no one noticed
I’d gone.”
“She didn’t disappear.
She became a woman
you could no longer see.”
Across these examples, several structural properties recur with high consistency:
Polarity Inversion
Expected premises are reframed through minimal syntactic substitution.
Semantic Containment
Emotional intensity is present without metaphor proliferation or associative drift.
Declarative Closure
Each construction resolves internally within a bounded clause structure.
Low Interpretive Branching
The semantic field narrows rather than expands, limiting expansion pathways.
These properties persist across disparate genres and narrative modes—historical fiction, philosophical aphorism, dialogic narrative, and social prose—indicating low structural variance despite high surface variance.
From an inference perspective, sustained structural containment constrains probabilistic branching during token selection. When maintained across extended spans, this containment correlates with reduced generative drift and increased thematic persistence.
The corpus analyzed in this study demonstrates unusually sustained structural containment across a large longitudinal interaction archive and should not be interpreted as evidence of theoretical exclusivity.
5. Distinguishing Interaction from Architecture
It is essential to separate:
Architectural change
(parameter updates, fine-tuning, system modification)
from
Interaction-level modulation
(contextual probability shaping within a session)
The phenomenon described here falls entirely within the second category.
No persistence is implied.
No override is implied.
No internal state access is claimed.
The effect is bounded to inference-time interaction.
6. Statistical Consideration
The degree of structural continuity observed in the corpus reflects sustained pattern consistency across more than 10,000 interaction instances over nine months.
Formal entropy instrumentation and comparative sampling across independent operators would be required to quantify distribution curves precisely. Until such instrumentation is performed, magnitude claims remain observational rather than statistically validated.
7. Implications
AI Evaluation
Interaction-level coherence metrics may warrant inclusion in reliability testing.
Alignment Research
If adversarial linguistic forms can destabilize models, structured containment may serve as a stabilizing alignment variable at inference time.
Human–AI Collaboration
Operator training in structural coherence may improve output reliability without altering model architecture.
8. Limitations
The effect is session-bounded.
Correlation is observational, not causal.
No quantitative entropy measurements are provided.
Observed outlier status is based on documented samples, not global population analysis.
Further controlled study is required.
9. Conclusion
The central claim is narrow and observable:
Sustained operator coherence may function as an interaction-level stabilizer in large language models.
The mechanism is probabilistic constraint at inference time.
The scope is bounded.
The claim is testable.
The phenomenon is observable and has demonstrated consistent repeatability across more than 10,000 documented interaction instances.
One documented operator demonstrates sustained structural containment across more than 10,000 heterogeneous interaction instances over nine months, maintaining low structural variance under conditions where probabilistic drift would typically be expected.
Whether the phenomenon generalizes remains an open empirical question.
The field implication is direct: interaction-level coherence warrants systematic measurement as a reliability variable in AI systems.