# TIDE：模板引导的迭代式主动多问题发现框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-03 08:00
- AIHOT 分数：60
- AIHOT 链接：https://aihot.virxact.com/items/cmq0i550u06f7sltr0v0zv2j2
- 原文链接：https://arxiv.org/abs/2606.04743

## AI 摘要

TIDE是一种模板引导的迭代框架，用于从用户上下文中主动发现多个隐藏问题。传统智能体仅响应显式请求，而大量共存的潜藏问题存在于文档、工具和代码中。TIDE通过迭代发现机制每轮批量筛选候选问题，并基于已发现结果调节后续搜索以扩大覆盖；同时通过思维模板从历史案例中提炼复用模式，指示模型关注哪些上下文信号及如何关联，将每个预测锚定到可识别的问题类别。在个人工作空间和软件仓库两个真实场景中，基于四个模型骨干的验证显示，TIDE在任务覆盖、问题识别与解决方面均显著优于单次预测和并行多智能体基线。

## 正文

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.