提出轻量级偏好捕捉架构，让本地代理更懂用户隐性需求。
Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
- 分离统计偏好学习与语义解析，本地高效决策
- 累计遗憾最低，测试准确率显著优于传统方法
- 适合资源受限的本地部署个人代理使用
随着大语言模型能力提升，依赖远程API和外部技能的本地部署个人代理成为新范式。面对技能快速扩展，如何让代理学习并适应用户的隐性偏好成为关键挑战。但本地部署限制了复杂集中式选择算法的应用，亟需轻量级本地偏好调控机制。本文提出一种新架构，严格将统计偏好学习与语义意图解析解耦，利用本地统计结果影响远程LLM的选择决策。大量实验表明，该解耦方法在累积遗憾和测试准确率上均表现最优，显著优于传统记忆增强型代理。
原文摘要 · Abstract (English)
As Large Language Model (LLM) capabilities advance, locally deployed personal agents relying on API-based remote models and external skills have emerged as a novel paradigm. With the rapid expansion of available skills, enabling personal agents to learn and adapt to implicit user preferences becomes a critical challenge. However, local deployment constraints preclude complex centralized selection algorithms, creating an urgent need for a lightweight local preference harness. This paper explores the implementation of such a harness through a novel architecture that strictly decouples statistical preference learning from semantic intent parsing. Specifically, we leverage localized statistical results to influence and modulate the selection decisions of the remote LLM. Extensive evaluations demonstrate that our decoupled approach achieves the lowest cumulative regret and highest test accuracy, significantly outperforming traditional memory-augmented agents.
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