发表机构
Airalogy（艾拉罗）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统聊天界面被动响应的局限，提出主动式人工智能框架，通过可重新规划的临时消息队列管理待定对话动作，实现自我发起、持续跟进与动态修订，支持长期人机交互。
AI 中文摘要
传统的大语言模型聊天界面通常遵循用户发起的轮次：每个请求引发一个响应，之后系统等待进一步输入。而人类的异步交流则通过消息突发、延迟、沉默、话题恢复和自我发起的联系随时间展开。因此，主动式对话不仅需要决定何时说话，还需要管理待处理的对话动作，并允许随着上下文演变对其进行修订。我们引入了主动式人工智能（Proactive AI），这是一个围绕可重新规划的临时消息队列构建的主动对话框架。该框架将交互视为连续的事件时间线，将未发送的对话动作视为可修订的状态。用户事件、对话起搏器事件或计划决策可能产生沉默、一条或多条即时消息，或未来的对话动作。系统也可能观察到用户消息而不立即做出可见响应，并推迟响应决策。在投递之前，每个计划消息都必须根据当前上下文重新考虑；到达计划时间本身并不授权投递。我们提供了一种形式语义，刻画了未来对话动作在上下文演变中何时可以被修订、撤回或执行。该框架将人工智能的参与扩展到对当前请求的响应之外，使得无需新请求即可进行自我发起的交流、随时间持续跟进，以及随着上下文演变对后续动作进行修订。它为辅导、科学协作和日常陪伴中的长期人机交互提供了可执行的基础。
英文摘要
Conventional large-language-model chat interfaces typically follow user-initiated turns: each request elicits a response, after which the system waits for further input. Human asynchronous communication instead unfolds over time through message bursts, delays, silence, resumed topics, and self-initiated contact. Proactive dialogue therefore requires not only deciding when to speak, but also managing pending conversational actions and allowing them to be revised as context evolves. We introduce Proactive AI, a framework for proactive dialogue built around replannable temporal message queues. The framework treats interaction as a continuous event timeline and unsent conversational actions as revisable state. A user event, dialogue-pacemaker event, or scheduled decision may yield silence, one or more immediate messages, or future conversational actions. The system may also observe a user message without an immediate visible response and defer the response decision. Before delivery, every planned message must be reconsidered against the current context; reaching a scheduled time does not itself authorize delivery. We provide a formal semantics that characterizes when future conversational actions may be revised, withdrawn, or executed as context evolves. The framework extends AI participation beyond responses to current requests, enabling self-initiated exchanges without new requests, sustained follow-up over time, and revisions of subsequent actions as context evolves. It provides an executable basis for long-term human-AI interaction in tutoring, scientific collaboration, and everyday companionship.
Comments17 pages, 3 figures and 1 supplementary figure
