ValuePilot：一种面向价值驱动决策的双阶段框架
ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making
AI总结：
ValuePilot通过双阶段框架实现价值驱动的个性化决策，提升AI在复杂场景中的可解释性和适应性。
摘要：
在人机交互中，个性化决策至关重要，使AI代理能够与用户的价值偏好保持一致。随着AI系统扩展到现实世界应用，适应超越任务完成或集体一致性的个性化价值成为关键挑战。我们通过提出一种以价值为导向的个性化决策方法来解决这一问题。人类价值观作为稳定、可转移的信号，支持在不同情境中的一致和可推广行为。与以外部奖励和激励为导向的任务导向范式相比，价值驱动的决策增强了解释性，并使代理能够在新场景中做出适当的行为。我们引入了ValuePilot，一个由数据集生成工具包（DGT）和决策模块（DMM）组成的双阶段框架。DGT通过人类-LLM协作流程构建多样化的、带有价值标注的场景。DMM学习根据个人价值偏好评估行动，实现情境敏感的个性化决策。在评估先前未见过的场景时，DMM在与人类行动选择一致方面优于强大的LLM基线，包括GPT-5、Claude-Sonnet-4、Gemini-2-flash和Llama-3.1-70b。我们的结果表明，价值驱动的决策是构建可解释、个性化的AI代理的有效且可扩展的工程路径。
英文摘要：
Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-world applications, adapting to personalized values beyond task completion or collective alignment has become a critical challenge. We address this by proposing a value-driven approach to personalized decision-making. Human values serve as stable, transferable signals that support consistent and generalizable behavior across contexts. Compared to task-oriented paradigms driven by external rewards and incentives, value-driven decision-making enhances interpretability and enables agents to act appropriately even in novel scenarios. We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM). DGT constructs diverse, value-annotated scenarios from a human-LLM collaborative pipeline. DMM learns to evaluate actions based on personal value preferences, enabling context-sensitive, individualized decisions. When evaluated on previously unseen scenarios, DMM outperforms strong LLM baselines, including GPT-5, Claude-Sonnet-4, Gemini-2-flash, and Llama-3.1-70b, in aligning with human action choices. Our results demonstrate that value-driven decision-making is an effective and extensible engineering pathway toward building interpretable, personalized AI 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。
