arXiv cs.CL· Zhi Liu, Yurun Cai, Xiao Han, Zhi Zhen, Yuran Li, Guojiang Shen, Xiangjie Kong·· 13 小时前AI 评分20
TAGR：跨异构城市配送任务的任务自适应地理语义检索框架
TAGR: Task-Adaptive Geo-Semantic Retrieval across Heterogeneous Urban Delivery Tasks
AI 导读
TAGR 是一个面向城市配送的任务自适应地理语义检索框架，用 LLM 构建地理语义共享先验与任务专属检索库，在共享区域知识的同时保留各任务异构证据。它引入任务自适应检索增强模块适配不同输出结构，缓解统一检索融合带来的信息错配与负迁移，且无需在线 LLM 推理。在三个城市数据集上，TAGR 在需求预测、到达时间预测和下一站预测等任务上均优于 SOTA 基线。
正文 · 原文
Abstract:As urban residents demand higher-quality instant delivery services, demand forecasting, estimated time of arrival prediction, and next-stop prediction have become critical tasks in urban delivery systems. However, the three tasks rely on historical evidence with substantially different structures and granularities. Meanwhile, their prediction targets differ in output form, making it difficult to integrate retrieved evidence through a single fusion mechanism without causing mismatch or negative transfer. These issues can increase prediction errors and subsequently lead to imbalanced resource allocation, where some regions experience unmet demand. In contrast, others suffer from underutilization, ultimately degrading overall efficiency and service quality. To address these challenges, we propose TAGR, a Task-Adaptive Geo-Semantic Retrieval framework across heterogeneous urban delivery tasks. TAGR constructs an LLM-based geo-semantic shared prior and task-specific retrieval banks to share regional knowledge while preserving heterogeneous task-specific evidence. The LLM-derived regional representations encode geographic context and semantic relations among regions, providing transferable knowledge for all three tasks without requiring online LLM inference. It further introduces a task-adaptive retrieval augmentation module to accommodate heterogeneous output structures, thereby mitigating information mismatch and negative transfer caused by a uniform retrieval-fusion strategy. Experiments on three city datasets demonstrate that TAGR consistently outperforms state-of-the-art baselines across multiple tasks.
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.28638 [cs.IR] |
| (or arXiv:2610.28638v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28638 arXiv-issued DOI via DataCite |
Submission history
From: Cai Yurun [view email]
[v1]
Wed, 12 Aug 2026 05:25:25 UTC (312 KB)
来源：arXiv cs.CL · arxiv.org