RPM: Reasoning-Level Personalization for Black-Box Large Language Models

ICLR 2026 Conference Submission18279 Authors

Keywords: Personalization, Large Language Model, Reasoning-Level Personalization, LLM, LLM Personalization, Black-Box LLM

Abstract:

While black-box large language models are widely deployed, they produce generic outputs that overlook individual user preferences.
Current personalization methods are fundamentally limited to response-level personalization; they only match final outputs, failing to model the underlying reasoning that connects user behavior to responses.
To address this, this work introduces reasoning-level personalization as a new paradigm and proposes RPM, the first systematic framework designed to guide the model’s reasoning process using structured rationales constructed from patterns in a user’s behavior.
RPM constructs a structured model of user behavior—built from response-influential features and statistical factors—to create personalized reasoning paths and retrieve beneficial examples for guiding inference through a feature-based retrieval mechanism.
Extensive experiments across four diverse tasks demonstrate that RPM consistently outperforms existing response-level methods while simultaneously enhancing both personalization performance and interpretability, providing a promising direction for black-box LLM personalization.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 18279