AI 中文总结
本研究提出G-STEER方法，通过图支架证据锚定优化用户请求为个性化研究规范，在提升深度研究智能体报告个性化的同时，大幅减少向用户提问的数量。
AI 中文摘要
用户请求是深度研究智能体（DRA）的研究规范，决定了需要获取的证据及合成方式。在个性化深度研究中，这些规范还需反映用户的目标、约束、偏好和评估标准。用户上下文可嵌入深度研究流程内部，或作为输入提供的研究规范。本文聚焦后者，将用户请求优化为个性化研究规范后再输入未改动的深度研究智能体，需解决三个耦合决策：哪些框架因素相关、可用用户上下文是否足够支撑这些因素、是检索用户记忆、询问用户还是停止并优化查询。训练时，G-STEER将框架因素组织为意图引出图中的引出目标，该图捕获因素间依赖关系；它从涵盖不同因素依赖和证据条件的图支架轨迹中学习澄清策略，该策略生成优化查询时平衡目标覆盖与证据获取成本。实验显示，G-STEER在两种评估的深度研究智能体上均实现最强的整体加权目标覆盖和最高的下游报告个性化，且询问用户的问题数仅为强澄清基线的约三分之一。
英文摘要
User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it. In personalized deep research, these specifications must additionally reflect user goals, constraints, preferences, and evaluation criteria. User context can be incorporated either within the deep research pipeline or into the research specification provided as its input. We focus on the latter, refining the user request into a personalized research specification before passing it to an unchanged deep research agent. This requires resolving three coupled decisions: which framing factors are relevant, whether the available user context sufficiently supports them, and whether to retrieve user memory, ask the user, or stop and refine the query. For training, G-STEER organizes framing factors as elicitation targets in an Intent Elicitation Graph that captures their dependencies. It learns a clarification policy from graph-scaffolded trajectories spanning diverse factor dependencies and evidence conditions. The policy produces a refined query while balancing target coverage against the costs of evidence acquisition. Experiments show that G-STEER achieves the strongest overall weighted target coverage and the highest downstream report personalization across both evaluated DRAs, while asking roughly one third as many user questions as a strong clarification baseline.
Comments13 pages, 4 figures
