TIDE：基于模板引导迭代的主动式多问题发现

摘要

智能体被广泛部署为文档、工具和代码的助手。然而，它们通常仅对显式用户请求做出响应，而这些请求只反映了用户已注意到的问题，与此同时，许多其他重要问题共存于更广泛的用户上下文中，隐藏在显而易见之处，其总数事前未知。我们将此定义为从上下文中发现多个隐藏问题的任务——需揭示共存问题，将其锚定于支撑证据，并配以具体行动。为此，我们提出TIDE，一个模板引导的迭代框架，包含两种互补机制。具体而言，基于“单次预测倾向于聚焦最显著案例并产生泛化断言”这一观察，我们提出迭代发现机制：每轮揭露一小批候选，同时基于已有发现进行条件化，使后续轮次扩展覆盖范围；以及思维模板机制：从先前解决的案例中提炼出可复用的模式，指明应关注哪些上下文信号以及如何连接它们，将每次预测锚定于一个可识别的问题类别。我们在个人工作空间与软件仓库两个现实场景中，基于四种模型主干对TIDE进行验证，结果显示其在任务覆盖率、问题识别与解决方面均大幅优于单次预测和并行多智能体基线。

English

Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has noticed, while many other important problems coexist, hidden in plain sight, within the broader user context, with their total number unknown in advance. We frame this as the task of discovering multiple hidden problems from context, in which coexisting problems should be uncovered, grounded in supporting evidence, and paired with concrete actions. To this end, we introduce TIDE, a template-guided iterative framework with two complementary mechanisms. Specifically, motivated by the observation that single-pass prediction anchors on the most salient cases and yields generic claims, we propose iterative discovery, which surfaces a small batch of candidates per round while conditioning on what has already been found, so subsequent rounds extend coverage; and thought templates, reusable schemas distilled from previously solved cases that specify what contextual signals to attend to and how to connect them, anchoring each prediction in a recognizable problem class. We validate TIDE on two realistic settings, personal workspaces and software repositories, across four model backbones, showing substantial gains over single-shot and parallel multi-agent baselines on task coverage, identification, and resolution.