6 Application Index · 6.5 AI Runtime Personalization
6.5 AI Runtime Personalization
Human Behavior FCA
This page is structured as definition, control variables, causal chain, observable outputs, and boundary, and serves as a canonical definition node in Human Behavior FCA.
AI runtime personalization is long-term state modeling, not chat-style decoration.
Its target is cross-cycle tracking of input, output, breakpoints, and feedback-update trajectory.
Its value lies in building a persistently updatable long-term state system.
Long-horizon user output logs.
Breakpoint history distribution.
Feedback-update record quality.
Current foreground-input structure.
Cross-cycle state continuity.
Observable Output → Foreground Entry → Historical Template Matching and Explanation → New Structure Generation → Execution Maintenance → Feedback Update.
AI runtime personalization requires per-cycle chain localization and feedback write-back into state layer.
If responses stay single-cycle without chain-state persistence, real personalization is not formed.
The system can continuously reference historical breakpoints with node-level localization.
Recommendations for similar issues structurally shift with feedback update.
Cross-cycle output consistency improves while recurring error loops decline.
It is not a prompt-wrapper layer.
It is not a standard chatbot shell.
It is not one-shot QA, and it does not expose backend scoring parameters.