用信息价值框架让智能体自动判断何时该问问题，减少无效沟通。
Value of Information: A Framework for Human-Agent Communication
- 基于信息价值理论，动态权衡提问收益与用户负担。
- 在医疗等高风险场景下，比人工调参方法多获1.36分效用。
- 无需调参，适配游戏到医疗等多种任务场景。
部署于真实任务的大语言模型智能体面临根本性困境：用户请求通常不完整，但智能体必须决定是基于不充分信息行动，还是中断用户以获取澄清。现有方法要么依赖需任务特调的脆弱置信阈值，要么忽略不同决策的差异代价。本文提出一种决策论框架——信息价值（VoI），使智能体能动态权衡提问带来的预期效用增益与对用户的认知成本。该推理时方法无需超参数调优，可无缝适应从休闲游戏到医疗诊断的多种情境。在四个多样化领域（20个问题、医疗诊断、航班预订、电商）的实验表明，VoI始终匹配或超越最佳手动调参基线，在高代价场景中最高提升1.36效用点。本工作提供了一个无参的自适应智能体通信框架，显式平衡任务风险、查询模糊性与用户努力。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete information or interrupt users for clarification. Existing approaches either rely on brittle confidence thresholds that require task-specific tuning, or fail to account for the varying stakes of different decisions. We introduce a decision-theoretic framework that resolves this trade-off through the Value of Information (VoI), enabling agents to dynamically weigh the expected utility gain from asking questions against the cognitive cost imposed on users. Our inference-time method requires no hyperparameter tuning and adapts seamlessly across contexts-from casual games to medical diagnosis. Experiments across four diverse domains (20 Questions, medical diagnosis, flight booking, and e-commerce) show that VoI consistently matches or exceeds the best manually-tuned baselines, achieving up to 1.36 utility points higher in high-cost settings. This work provides a parameter-free framework for adaptive agent communication that explicitly balances task risk, query ambiguity, and user effort.
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