Deep Personalization Output Study

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