arXiv AI June 30, 2026 By Chenyou Guo, Zongqi Liu, Yizhou Zhang, Zhaorui Jiang, Ze Liu

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 12

GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models

arXiv:2606. 12821v1 Announce Type: new Abstract: Environmental scientists spend disproportionate effort on data wrangling rather than analysis, and AI agents that automate geospatial workflows remain unvalidated: no benchmark evaluates agents operating through structured tool calling against real APIs.

By Gabriel Diaz-Ireland, Diego Prieto-Herr\'aez, Mario Garc\'ia Peces, Javier Vel\'azquez, Devika Jain

llms agents benchmarks

arXiv AI

Jul 28

GeoDecider: An Evidence-Grounded Agent for Geological Interpretation via Deliberative Reasoning

arXiv:2605. 03383v2 Announce Type: replace Abstract: Geological interpretation infers subsurface properties and structures from indirect geophysical observations.

By Xiaoyu Tao, Mingyue Cheng, Jiahao Wang, Yitong Zhou, Qingyang Mao, Yimin Dou, Qi Liu, Shijin Wang, Enhong Chen

agents benchmarks

arXiv AI

Jun 12

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?

arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.

By Dat Tien Nguyen, Thao Nguyen, Fadillah Adamsyah Maani, Huy M. Le, Muhammad Umer Sheikh, Numan Saeed, Muhammad Haris Khan, Salman Khan

llms agents benchmarks

arXiv AI

Jul 20

GeoChrono: Benchmarking and Rethinking Long-Term Temporal Understanding in Remote Sensing

arXiv:2607. 15768v1 Announce Type: cross Abstract: Remote sensing offers an unparalleled vantage point for observing the Earth's long-term surface evolution, yet it demands that a model not only perceive land cover at isolated moments, but also track changes, memorize evolution histories, and reason across time and space.

By Yujie Li, Jiancheng Pan, Zhiwei Wei, Jiuniu Wang, Mugen Peng, Wenjia Xu

benchmarks

arXiv AI

Jul 21

Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning

arXiv:2505. 20161v2 Announce Type: replace-cross Abstract: Effective generalization in language models depends critically on the diversity of their training data.

By Jaehun Jung, Seungju Han, Ximing Lu, Skyler Hallinan, David Acuna, Shrimai Prabhumoye, Mostafa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi

llms benchmarks

arXiv Machine Learning

Jul 31

Divergence Decoding: Training-Free Capability Fusion

arXiv:2607. 27248v1 Announce Type: cross Abstract: While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains.

By Yimi Wang, Hao Li, Shuo Yang, He Cao, Dechen Zhang, Ziang Wu, Zhiyuan Yan, Fanyang Mo, Li Yuan

llms benchmarks

Related stories

GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models

GeoDecider: An Evidence-Grounded Agent for Geological Interpretation via Deliberative Reasoning

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?

GeoChrono: Benchmarking and Rethinking Long-Term Temporal Understanding in Remote Sensing

Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning

Divergence Decoding: Training-Free Capability Fusion