让黑箱大模型学会按用户思维推理，更懂你。
RPM: Reasoning-Level Personalization for Black-Box Large Language Models
- 从用户行为中自动提取个性化推理路径
- 在四项任务上均优于传统方法，提升可解释性
- 适合需要精准个性化的智能交互场景
尽管黑箱大语言模型被广泛应用，但其输出普遍缺乏对用户偏好的考量。现有个性化方法仅停留在响应层面，仅匹配最终输出，未能建模连接用户行为与回应的深层推理过程。为此，本文提出一种新的推理级个性化范式，并设计RPM框架，首次实现从原始行为数据中自动发现用户专属的推理结构，以指导模型个性化推理。RPM通过特征化检索机制构建用户行为结构——包含影响响应的特征与统计因素——生成个性化推理路径并检索有益示例，引导推理过程。在四个多样化任务上的广泛实验表明，RPM持续优于现有响应级方法，同时显著提升个性化性能与可解释性，为黑箱大模型个性化提供新方向。
原文摘要 · Abstract (English)
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 that automatically discovers user-specific reasoning structures from raw behavioral data to guide the model's personalized inference. 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.
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