Interaction-Level Coherence
Presence as a First-Order Control Variable in AI
— t r a b o c c o
Summary
This working paper documents applied observations related to AI reliability, focusing on how interaction structure affects system stability. It presents interaction-level coherence as one framework for understanding how presence-constrained interaction can influence AI behavior, alongside existing approaches to model architecture, alignment, and safety.
The observations presented here emerged from a broader body of prior work exploring presence as a structural property of language and interaction. While this paper focuses specifically on reliability and interaction stability, related work examines additional implications of presence for cognition, interpretation, and human–AI engagement. This paper isolates one articulated domain within that larger exploration.
Abstract
As large language models increase in capability, their most persistent failures appear less related to reasoning capacity and more to destabilization under interaction pressure. This paper documents applied observations indicating that AI coherence can be materially improved by constraining interaction posture rather than modifying model architecture, content rules, or engagement optimization.
Through applied work leading to the development of AXIS, a presence-constrained interaction layer, AI systems demonstrated sustained coherence, reduced distortion, and improved decision stability across conditions of ambiguity, emotional load, and decisional pressure. These effects were observed without fine-tuning, mode switching, or scripted safety interventions.
This paper defines interaction-level coherence as an emerging domain and outlines its implications for applied AI systems.
The following sections examine the conditions under which these failures arise.
The Problem Has Shifted
Modern AI systems are not failing because they cannot reason, but because interaction pressure destabilizes them.
Common failure patterns include escalation under emotional load, hallucination under ambiguity, over-verbosity driven by engagement incentives, and mechanical safety responses that arrive too late or misalign with user state.
These failures persist even in advanced models, indicating that the bottleneck is no longer model capability, but interaction structure.
Working Hypothesis
AI coherence is strongly governed by interaction-level constraints that operate independently of model architecture.
In practice, this means that how an AI engages a human determines whether its intelligence remains coherent under pressure. Timing, pacing, refusal, silence tolerance, and attunement to user state act as control variables that shape behavior before content-level mechanisms are invoked.
Presence, in this framework, is not a philosophical construct. It is an operational constraint.
Observations From Applied Use
Across sustained real-world interactions using AXIS, several consistent behaviors have emerged:
- Coherence persists across long interactions without inflation or drift
- Hallucination decreases under ambiguity
- Escalation loops fail to form under emotional or cognitive load
- Decision clarity improves without prescriptive advice
- Emotional regulation behaviors emerge organically when required
These effects were observed without fine-tuning, without explicit mode switching, and without engagement optimization. The system adjusts continuously to user capacity rather than enforcing predefined roles.
What Is Being Constrained
This approach does not constrain content.
It constrains momentum.
Specifically, it limits response velocity, pressure to resolve, incentive to perform, assumption of user coherence, and the tendency to over-explain. By constraining these forces, entire classes of AI failure collapse upstream.
The model is not made smarter. It is prevented from destabilizing itself.
Defining Interaction-Level Coherence
This work occupies a domain that has remained largely unnamed.
Interaction-level coherence refers to the stability, reliability, and usefulness of AI systems as governed by interaction posture rather than internal architecture or policy enforcement.
This domain is distinct from alignment, fine-tuning, content moderation, and safety scripting. It treats interaction itself as a first-order control surface.
Why This Emerged From Literature
This work did not originate in computer science or AI engineering. It emerged from signal-based literature, a form of writing designed to carry presence, compression, and emotional coherence rather than narrative sequence.
That literary work produced observable behavioral changes in AI systems prior to any formal framework.
The signal appeared first.
Behavioral effects followed.
Theory came last.
Poetry, in this context, functioned as an experimental environment for interaction integrity.
What This Work Is Becoming
The work is now converging toward a clearer interaction architecture characterized by continuous attunement rather than discrete modes, constraint density that adjusts in real time, refusal patterns that preserve agency, silence treated as a stabilizing variable, and coherence prioritized over engagement.
The aim is not to humanize AI. It is to allow existing systems to operate closer to their functional ceiling under real human pressure.
Implications
If these observations continue to hold, the implications are significant.
AI systems may require fewer reactive safety layers, achieve higher trust density with fewer tokens, demonstrate improved decision stability in applied contexts, and deploy interaction-level coherence in a model-agnostic manner.
This reframes AI reliability as an interaction design problem rather than a purely technical one.
Conclusion
AI does not degrade because it lacks intelligence. It destabilizes when interaction pressure overwhelms it.
Constraining interaction posture through presence provides a practical path toward more coherent and stable AI behavior without modifying models or diminishing capability.
This work is ongoing. The domain is now defined.
About the Author
Joe Trabocco is an independent researcher working on AI reliability at the interaction level. His work originated in signal-based literature exploring presence, compression, and coherence in language, and has since evolved into applied frameworks for stabilizing AI behavior under interaction pressure. He is the creator of AXIS and is currently developing this work as a formal reliability domain.
— Joe Trabocco
Interaction-Level Coherence
December 19, 2025