隐式偏好的统计先验：在个人代理中解耦技能选择作为局部调控机制
Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
AI总结：
针对本地部署的个人代理中隐式用户偏好学习问题，提出一种解耦统计偏好学习与语义意图解析的轻量级架构，通过局部统计结果影响远程LLM的选择决策，显著降低累积遗憾并提高测试准确率。
摘要：
随着大型语言模型（LLM）能力的提升，依赖基于API的远程模型和外部技能的本地部署个人代理成为一种新范式。随着可用技能的快速扩展，使个人代理能够学习并适应隐式用户偏好成为关键挑战。然而，本地部署的限制排除了复杂的集中式选择算法，迫切需要一种轻量级的局部偏好调控机制。本文通过一种严格解耦统计偏好学习与语义意图解析的新颖架构，探索了这种调控机制的实现。具体而言，我们利用局部统计结果来影响和调节远程LLM的选择决策。大量评估表明，我们的解耦方法实现了最低的累积遗憾和最高的测试准确率，显著优于传统的记忆增强型代理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
