IntPro：一种通过检索条件推理的代理代理，用于基于情境的意图理解
IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference
AI总结：
IntPro通过检索条件推理学习适应个体用户，提升基于情境的意图理解性能。
摘要：
大型语言模型（LLMs）已成为现代人机协作工作流程中的关键组成部分，其中准确理解用户意图是生成满意响应的关键步骤。基于情境的意图理解，即从情境环境中推断用户意图，本质上具有挑战性，因为它需要推理即时上下文和驱动用户行为的潜在动机。此外，现有方法通常将意图理解视为静态识别任务，忽略了用户积累的意图模式，这些模式可以为更准确和可推广的理解提供有价值的参考。为了解决这一差距，我们提出了IntPro，一种通过检索条件意图推理来适应个体用户的代理。我们设计了意图解释，抽象了情境信号如何连接到表达的意图，并将其存储在个体意图历史库中进行检索。我们通过监督微调在检索条件轨迹上训练IntPro，并通过多轮组相对策略优化（GRPO）和工具感知奖励函数进行训练，使代理能够学习何时利用历史意图模式，何时直接推断。在三个不同的场景（Highlight-Intent、MIntRec2.0和Weibo Post-Sync）中的实验表明，IntPro在不同场景和模型类型中实现了强大的意图理解性能，具有有效的基于情境的推理能力。
英文摘要：
Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user's underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users' accumulated intent patterns that could provide valuable references for more accurate and generalizable understanding. To address this gap, we propose IntPro, a proxy agent that learns to adapt to individual users via retrieval-conditioned intent inference. We design intent explanations that abstract how contextual signals connect to expressed intents, and store them in an individual intent history library for retrieval. We train IntPro through supervised fine-tuning on retrieval-conditioned trajectories and multi-turn Group Relative Policy Optimization (GRPO) with tool-aware reward functions, enabling the agent to learn when to leverage historical intent patterns and when to infer directly. Experiments across three diverse scenarios (Highlight-Intent, MIntRec2.0, and Weibo Post-Sync) demonstrate that IntPro achieves strong intent understanding performance with effective context-aware reasoning capabilities across different scenarios and model types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
