A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis

• URL: http://arxiv.org/abs/2506.14345v1
• Date: Tue, 17 Jun 2025 09:38:45 GMT
• Title: A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis
• Authors: Bruno Martins, Piotr Szymański, Piotr Gramacki,
• Abstract summary: Current deep research systems lack the geo-temporal capabilities that are essential for answering context-rich questions.<n>This paper identifies important technical, infrastructural, and evaluative challenges in integrating geo-temporal reasoning into deep research pipelines.
• Score: 1.6834474847800562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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