mlsanityData to 14 Oct 2026

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

arXiv 2506.14345 ↗First posted 17 Jun 2025cs.CL cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 32 of its 54 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

32 of 54 references.

References · 32

  1. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  2. 2024A Survey on LLM-as-a-Judgecited by 1,423
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  4. 2023GAIA: a benchmark for General AI Assistantscited by 1,128
  5. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
  6. 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 465
  7. 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 425
  8. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  9. 2024Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generationcited by 162
  10. 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 77
  11. 2023K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilizationcited by 69
  12. 2024FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructionscited by 69
20 more
  1. 2025SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesiscited by 55
  2. 2023Autonomous GIS: the next-generation AI-powered GIScited by 53
  3. 2025DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Researchcited by 53
  4. 2025An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agentscited by 46
  5. 2023GeoLM: Empowering Language Models for Geospatially Grounded Language Understandingcited by 37
  6. 2023GeoGalactica: A Scientific Large Language Model in Geosciencecited by 33
  7. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Trackcited by 32
  8. 2024Long2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recallcited by 32
  9. 2025RaDeR: Reasoning-aware Dense Retrieval Modelscited by 27
  10. 2024Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agentscited by 23
  11. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23
  12. 2025TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questionscited by 15
  13. 2025Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judgescited by 14
  14. 2025A Study into Investigating Temporal Robustness of LLMscited by 11
  15. 2025Towards Better Instruction Following Retrieval Modelscited by 9
  16. 2024Measuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4cited by 8
  17. 2024RAGProbe: An Automated Approach for Evaluating RAG Applicationscited by 7
  18. 2024Discourse-Aware In-Context Learning for Temporal Expression Normalizationcited by 2
  19. 2025Evaluating Answer Reranking Strategies in Time-sensitive Question Answeringcited by 2
  20. 2025The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical featurescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Privacy Without Losing Place: A Paradigm for Private Retrieval in Spatial RAGscited by 0
  2. 2026Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seekingcited by 0

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  1. 2024Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval
  2. 2026GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
  3. 2025Deep Research: A Systematic Survey
  4. 2025The Role of Open-Source LLMs in Shaping the Future of GeoAI
  5. 2025A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
  6. 2025Deep Research: A Survey of Autonomous Research Agents
  7. 2025Around the World in 24 Hours: Probing LLM Knowledge of Time and Place
  8. 2026MapVerse: A Benchmark for Geospatial Question Answering on Diverse Real-World Maps