Understanding user intent is essential for effective planning in conversational assistants, particularly those powered by large language models (LLMs) coordinating multiple agents. However, real-world dialogues are often ambiguous, underspecified, or dynamic, making intent detection a persistent challenge. Traditional classification-based approaches struggle to generalize in open-ended settings, leading to brittle interpretations and poor downstream planning. We propose RECAP (REwriting Conversations for Agent Planning), a new benchmark designed to evaluate and advance intent rewriting, reframing user-agent dialogues into concise representations of user goals. RECAP captures diverse challenges such as ambiguity, intent drift, vagueness, and mixed-goal conversations. Alongside the dataset, we introduce an LLM-based evaluator that assesses planning utility given the rewritten intent. Using RECAP, we develop a prompt-based rewriting approach that outperforms baselines, in terms of plan preference. We further demonstrate that fine-tuning two DPO-based rewriters yields additional utility gains. Our results highlight intent rewriting as a critical and tractable component for improving agentic planning in open-domain dialogue systems.

翻译：理解用户意图对于对话助手（尤其是基于大语言模型（LLM）协调多智能体的系统）的有效规划至关重要。然而，现实世界中的对话往往存在歧义、表述不完整或动态变化的特点，这使得意图检测成为一个持续存在的挑战。传统的基于分类的方法在开放场景中难以泛化，导致意图解析脆弱且下游规划效果不佳。我们提出了RECAP（面向智能体规划的对话重写），这是一个旨在评估和推进意图重写的新基准，其核心是将用户-智能体对话重构为用户目标的简明表示。RECAP涵盖了歧义性、意图漂移、模糊性及多目标混合对话等多种挑战。除数据集外，我们还引入了一个基于LLM的评估器，用于评估重写后意图的规划效用。基于RECAP，我们开发了一种基于提示的重写方法，其在规划偏好方面优于基线模型。我们进一步证明，对两种基于DPO的重写模型进行微调可带来额外的效用提升。我们的研究结果表明，意图重写是提升开放域对话系统中智能体规划能力的关键且可实现的组成部分。