研究大模型在长期任务中是更关注当下还是未来，如何调控其时间偏好。
Temporal Preferences in Language Models for Long-Horizon Assistance
- 用人类实验方法测试模型在时间选择中的倾向性。
- 推理型模型在提示引导下更倾向未来选项，但个性化能力有限。
- 发现模型能内化自身为AI决策者的时间观，适合长期助手设计。
我们研究语言模型在跨期选择中是否表现出对未来或当下的偏好，以及这些偏好能否被系统性操控。采用改编的人类实验范式，评估多个语言模型在时间权衡任务中的表现，并与人类决策者样本对比。提出操作性指标“时间取向可操控性”（MTO），定义为模型在不同提示下揭示的时间偏好变化量。测试显示，以推理为核心的模型（如DeepSeek-Reasoner和grok-3-mini）在面向未来的提示下更倾向于选择延迟奖励，但在身份或地理情境下的个性化决策能力较弱。此外，能正确推理时间取向的模型会内化一种面向未来的自我认知，将其视为人工智能决策者。本文讨论了符合多元长期目标的AI助手设计启示，并提出个性化上下文校准与社会敏感部署的研究议程。
原文摘要 · Abstract (English)
We study whether language models (LMs) exhibit future- versus present-oriented preferences in intertemporal choice and whether those preferences can be systematically manipulated. Using adapted human experimental protocols, we evaluate multiple LMs on time-tradeoff tasks and benchmark them against a sample of human decision makers. We introduce an operational metric, the Manipulability of Time Orientation (MTO), defined as the change in an LM's revealed time preference between future- and present-oriented prompts. In our tests, reasoning-focused models (e.g., DeepSeek-Reasoner and grok-3-mini) choose later options under future-oriented prompts but only partially personalize decisions across identities or geographies. Moreover, models that correctly reason about time orientation internalize a future orientation for themselves as AI decision makers. We discuss design implications for AI assistants that should align with heterogeneous, long-horizon goals and outline a research agenda on personalized contextual calibration and socially aware deployment.
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