Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide integrated service recommendations, we focus on the task of Generative Spatiotemporal Intent Sequence Recommendation (GSISR), which aims to generate intent sequences that are logically coherent and physically executable within complex spatiotemporal contexts. While LLMs offer strong reasoning potential for GSISR, direct industrial deployment is limited by high inference latency and context-mismatched or physically infeasible plans. To address these challenges, we propose a generative framework, GPlan, that internalizes LLM reasoning into lightweight models through two components. First, to enable reasoning under strict latency constraints, we introduce Progressive Implicit CoT Distillation, which compresses explicit reasoning processes into reserved latent tokens, allowing small models to inherit complex planning logic without generating long reasoning text. Second, to address the disconnect between general knowledge and real-world constraints, we design Spatiotemporal Counterfactual DPO. By aligning the model with counterfactual context-plan pairs, we improve sensitivity to spatiotemporal context and reduce context-mismatched plans. Offline experiments and online A/B testing demonstrate that our approach improves sequence coherence and context responsiveness. Our implementation and the anonymized GSISR dataset are available at https://github.com/alibaba/GPlan.

翻译：真实世界中的用户行为很少由孤立动作构成，而是形成依赖时空关联的意图流。为提供集成式服务推荐，我们聚焦于生成式时空意图序列推荐（GSISR）任务，旨在生成逻辑连贯且可在复杂时空场景中物理执行的意图序列。虽然大语言模型（LLM）为GSISR提供了强大的推理潜力，但工业级部署受限于高推理延迟、上下文不匹配或物理不可行的规划方案。为应对这些挑战，我们提出生成式框架GPlan，通过两个组件将LLM推理能力内化至轻量模型：首先，为在严格延迟约束下实现推理，引入渐进隐式思维链蒸馏技术，将显式推理过程压缩至预留的隐式潜在表征中，使小模型无需生成冗长推理文本即可继承复杂规划逻辑；其次，为弥合通用知识与现实约束的脱节，设计时空反事实偏好优化（Spatiotemporal Counterfactual DPO）。通过使模型对齐反事实上下文-规划对，增强其对时空上下文的敏感度并减少上下文不匹配的规划方案。离线实验与在线A/B测试表明，该方法提升了序列连贯性与上下文响应能力。相关实现及匿名化GSISR数据集详见 https://github.com/alibaba/GPlan。