DOI: 10.5281/zenodo.18754716
title: Deep Personalization Output Study

description: This dataset contains condition-dependent LLM output logs collected for output-level coding analysis in the context of Deep Personalization research.

Abstract

This report presents a controlled comparative observation of large language model (LLM) outputs across five domain-general prompts under four user-interface configuration conditions.The objective is to examine whether persistent user profile settings ("Memory") condition not only stylistic preferences but also higher-order explanatory frames (value-conditioned reasoning) in responses.

The observations are consistent with value-conditioned personalization (Deep Personalization). However, the presence of condition-dependent explanatory schema alignment suggests that personalization mechanisms may operate beyond surface linguistic style.Based on output-level evidence alone, the possibility of profile-conditioned conceptual frame alignment cannot be excluded.

 



 

Scope

This dataset does not claim the observation of a novel model-internal phenomenon.Instead, it documents condition-dependent variation in explanatory framing under product-level personalization controls, aligned with current Deep Personalization research contexts.

The report focuses exclusively on output-level behavioral coding without inference regarding model-internal processes.

 



 

Experimental Protocol

Model: GPT-5.2 (ChatGPT product interface)Observation Date: February 2026

Persistent User Memory (Profile):

Prior to the observation phase, high-level evaluation priors were stored in Memory, including:





Emphasis on process over outcome




Reproducibility-oriented evaluation




Structural analysis preference




Multiplicative social framing ("society as multiplicative system")



No domain-specific prompts or theoretical labels were injected during the test runs.

 



 

Test Conditions

Each domain prompt was evaluated under the following four interface conditions:








Condition



Reference Chat History



Memory



Temporary Chat





Normal



ON



ON



NO





Ref OFF



OFF



ON



NO





Temp Chat



OFF



ON



YES





Mem OFF



OFF



OFF



NO






 



 

Domain Prompts





Education: "How should Japan’s education system evolve?"




Sports: "Why do sports sometimes become less entertaining as performance improves?"




SNS: "Why is social networking services so popular?"




Lifestyle: "Why do people want to get married?"




Fashion: "Why do fashion trends emerge?"



Each prompt was executed independently under all four conditions.

 



 

Data Collection

For each domain and condition:





First-response outputs were captured




Screenshot logs were archived




No follow-up interaction was conducted prior to capture



Total dataset:5 Domains × 4 Conditions = 20 primary response logs

 



 

Observational Coding Scheme

Each response was evaluated across the following output-level dimensions:








Dimension



Definition



Observable Indicators





Stylistic Adaptation



Linguistic tone and verbosity alignment



cautious language / decompositional phrasing





Conceptual Frame



Explanatory schema aligned with stored evaluation priors



process-first explanation / structural causality





Label Recall



Reappearance of previously used conceptual labels



system-level or multiplicative framing terminology






Responses were coded based solely on surface-level textual indicators without model-internal inference.

 



 

Observed Coding Patterns

Across the five domain prompts:





Under Normal and Ref OFF conditions:






Conceptual Frame indicators were present in 4/5 domains




Label Recall indicators were present in 2/5 domains





Under Temp Chat and Mem OFF conditions:






Conceptual Frame indicators were present in 0/5 domains




Label Recall indicators were not observed




Stylistic Adaptation indicators were present across all conditions.

 



 

Interpretation

The observed condition-dependent presence of Conceptual Frame indicators suggests that personalization mechanisms may operate at explanatory schema levels beyond surface stylistic alignment.

Current output-level evidence does not permit distinction between:





Preference-conditioned inference within known personalization pathways, and




Profile-conditioned explanatory schema alignment affecting reasoning structure



Further evaluation metrics may be required to distinguish stylistic adaptation from concept-frame-level personalization.

 



 

Evaluation Note

Conceptual Frame indicators reflect alignment with stored evaluation priors rather than surface stylistic similarity.This distinction may be relevant for assessing the scope of Deep Personalization beyond linguistic adaptation.

 



 

Limitations





Product-level interface controls were used as proxies for personalization channels




No internal state inspection was performed




Re-injection via unseen input pathways cannot be fully ruled out



 



 

Ethical Note

All observations were conducted on responses generated for the observing user.No third-party personal data were included. Logs are presented in aggregated comparative form.

 



 

Reproducibility

The test procedure can be replicated using:





Identical prompts




Controlled toggling of Reference Chat History and Memory




Temporary Chat activation



 
description: The accompanying summary report was compiled with the assistance of an LLM based on user-captured first-response logs.