Year: 2026
Type: conference-paper
Abstract: 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 understanding 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 1 (REwriting Conversations for Agentic 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 compares planning utility given a user-agent dialogue.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.
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140116418