This repository documents materials related to the Post-Optimization Observational Frame of Human–AI Interaction,
a neutral observational framework for recording state transitions that occur after optimization in human–AI interaction.
Within this frame, state transitions are recorded without reintroducing intention, meaning, purpose, or evaluation.
The framework does not model optimization processes or learning dynamics.
It records what transitions occurred and how structural differences between states can be observed.
The internal recording structure used within this observational frame has previously been referred to as State Transition Trace (STT).
Current and authoritative definitions are archived via OSF.
https://doi.org/10.17605/OSF.IO/5T9ZH
POF functions as an independent observational frame rather than a theory-bound explanatory system.
The formulation of the distribution change descriptor (Δφ) in this frame was informed by conceptual reasoning from the following project:
- sofience / operor_package_project
https://github.com/sofience/operor_package_project
This reference is included solely to preserve transparent conceptual lineage. It does not introduce interpretive claims, causal explanations, or modifications to the core definition of the Post-Optimization Observational Frame.