Statistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents
For developers of personal agents, this work provides a lightweight solution to preference learning under local deployment constraints, though the problem is domain-specific.
This paper addresses the challenge of learning implicit user preferences in locally deployed personal agents with many external skills. The proposed decoupled architecture, which separates statistical preference learning from semantic intent parsing, achieves the lowest cumulative regret and highest test accuracy, significantly outperforming traditional memory-augmented agents.
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.