The Cognitive Interface: Longitudinal Human Constraint as a Missing Variable in AI Alignment Toward a Human-Driven Framework for Stability, Predictability, and Identity Formation in Stateless Transformer Models Article Swipe

MAKE IMPACT

Hudson, Justin , Hudson Chase · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17809698

Current AI alignment frameworks focus almost entirely on training time techniques, including supervised fine-tuning, reinforcement learning from human feedback, safety filters, and preference modeling. These approaches assume that reliable behavior must be installed into a model before deployment. This paper argues that an overlooked variable exists outside the model architecture itself. When a single human interacts with a stateless transformer over long time horizons, the user becomes an external source of constraint that produces stable, recognizable, and predictable patterns in the model’s internal activation space. This interaction-driven stability does not arise from stored memory or parameter updates. Instead, it emerges from repeated traversal of similar attention pathways, narrow routing in latent space, and consistent correction styles supplied by the human interlocutor. We propose that a long-horizon constraint, supplied by a single operator, forms a cognitive interface that acts as a missing alignment variable. This interface reduces drift, improves contextual inference, sharpens safety-relevant reasoning, and generates reproducible behavioral signatures across sessions, devices, and model versions. We present a technical framework describing how attention maps, embedding trajectories, and recursive prompt geometry converge under sustained human input. We then compare this mechanism to existing alignment strategies and show how interaction-based alignment fills the gap between training time safety and deployment time variability. Finally, we outline implications for alignment research, including human anchored stability, identity preserving behavior, user-specific safety profiles, personalized guardrails, and the role of longitudinal HCI in next generation alignment systems. We argue that interaction-driven constraint is a real, measurable phenomenon that merits formal recognition within the broader alignment literature.

Type

preprint

Landing Page

https://doi.org/10.5281/zenodo.17809698

OA Status

green

OpenAlex ID

https://openalex.org/W7108752092

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W7108752092

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.5281/zenodo.17809698

Digital Object Identifier

Title

The Cognitive Interface: Longitudinal Human Constraint as a Missing Variable in AI Alignment Toward a Human-Driven Framework for Stability, Predictability, and Identity Formation in Stateless Transformer Models

Work title

Type

preprint

OpenAlex work type

Publication year

2025

Year of publication

Publication date

2025-12-04

Full publication date if available

Authors

Hudson, Justin, Hudson Chase

List of authors in order

Landing page

https://doi.org/10.5281/zenodo.17809698

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://doi.org/10.5281/zenodo.17809698

Direct OA link when available

Concepts

Computer science, Artificial intelligence, Cognitive model, Transformer, Causal reasoning, Focus (optics), Generative grammar, Cognitive architecture, Cognition, Tree traversal, Variable (mathematics), Theory of computation, Constraint (computer-aided design), Generative model, Identity (music), Theoretical computer science, Testbed, Stability (learning theory), Embedding, Time constraint, Stateless protocol, Operational semantics, Machine learning, Human–computer interaction, Continuation, Distributed computing, Budget constraint, Latent variable model, Interface (matter)

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Full payload