The "15-minute city" envisions neighborhoods where residents can meet daily needs via a short walk or bike ride. Realizing this vision requires not only physical proximity but also efficient and reliable access to information about nearby places, services, and events. Existing location-based systems, however, focus mainly on city-level tasks and neglect the spatial, temporal, and cognitive factors that shape localized decision-making. We conceptualize this gap as the Local Life Information Accessibility (LLIA) problem and introduce AskNearby, an AI-driven community application that unifies retrieval and recommendation within the 15-minute life circle. AskNearby integrates (i) a three-layer Retrieval-Augmented Generation (RAG) pipeline that synergizes graph-based, semantic-vector, and geographic retrieval with (ii) a cognitive-map model that encodes each user's neighborhood familiarity and preferences. Experiments on real-world community datasets demonstrate that AskNearby significantly outperforms LLM-based and map-based baselines in retrieval accuracy and recommendation quality, achieving robust performance in spatiotemporal grounding and cognitive-aware ranking. Real-world deployments further validate its effectiveness. By addressing the LLIA challenge, AskNearby empowers residents to more effectively discover local resources, plan daily activities, and engage in community life.

翻译：“15分钟城市”愿景旨在构建居民通过短途步行或骑行即可满足日常需求的社区。实现这一愿景不仅需要物理空间的邻近性，更需要高效可靠地获取周边场所、服务与活动的信息。然而，现有基于位置的系统主要聚焦城市级任务，忽视了影响本地化决策的空间、时间与认知因素。我们将此缺口概念化为“本地生活信息可及性”问题，并推出AskNearby——一款在15分钟生活圈内统一检索与推荐功能的AI驱动社区应用。AskNearby整合了（1）三层检索增强生成流程，协同实现基于图结构、语义向量与地理空间的检索；（2）编码用户社区熟悉度与偏好的认知地图模型。在真实社区数据集上的实验表明，AskNearby在检索准确率与推荐质量上显著优于基于大语言模型和传统地图的基线方法，在时空定位与认知感知排序方面均表现出鲁棒性能。实际部署进一步验证了其有效性。通过应对本地生活信息可及性挑战，AskNearby赋能居民更高效地发现本地资源、规划日常活动并参与社区生活。