让大模型具备地理时间推理能力，实现更精准的科研问答。
A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis
- 在检索与合成中引入地理时间约束处理机制
- 提出面向时空信息的可复现研究系统架构
- 适合从事环境、公共健康等时空分析的研究者
大型语言模型（LLMs）已推动深度研究系统发展，可通过规划性迭代搜索、检索与推理生成综合报告。然而，现有系统缺乏解决涉及地理和/或时间约束的复杂问题的能力，而这类问题在公共卫生、环境科学及社会经济分析中频繁出现。本文提出下一代研究系统的愿景，识别了将时空推理融入深度研究流程中的关键技术、基础设施与评估挑战。主张通过开放可复现的基础设施和严格的评估协议，增强检索与合成过程对时空约束的支持。该愿景勾勒出迈向更先进、具时空感知能力的深度研究系统的发展路径，有望推动人工智能驱动的信息获取未来。
原文摘要 · Abstract (English)
The emergence of Large Language Models (LLMs) has transformed information access, with current LLMs also powering deep research systems that can generate comprehensive report-style answers, through planned iterative search, retrieval, and reasoning. Still, current deep research systems lack the geo-temporal capabilities that are essential for answering context-rich questions involving geographic and/or temporal constraints, frequently occurring in domains like public health, environmental science, or socio-economic analysis. This paper reports our vision towards next generation systems, identifying important technical, infrastructural, and evaluative challenges in integrating geo-temporal reasoning into deep research pipelines. We argue for augmenting retrieval and synthesis processes with the ability to handle geo-temporal constraints, supported by open and reproducible infrastructures and rigorous evaluation protocols. Our vision outlines a path towards more advanced and geo-temporally aware deep research systems, of potential impact to the future of AI-driven information access.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。