Publications

You can also find my articles on my Google Scholar profile.

Book Chapters

On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust

Published in Geography According to Foundation Models, Vol. 422, IOS Press, 2026

Peer-reviewed book chapter reviewing key ethical issues in generative GeoAI. Wu authored Section 8, “Trust in AI and GeoAI Models,” covering geo-hallucination, uncertainty as an ethical requirement, and provenance-aware protocols.

Recommended citation: Mai, G., Lao, N., Zhang, J., Mao, L., Wang, Z., Wu, N., Janowicz, K., Wu, K., Rao, J., Gao, S., & Zhu, R. (2026). "On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust." In Geography According to Foundation Models, Vol. 422, pp. 215-232. IOS Press. DOI 10.3233/FAIA260483.
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Journal Articles

Diurnal and seasonal variations of meteor speed and arrival angle observed by Mengcheng meteor radar

Published in JGR: Space Physics, 2024

This study investigates diurnal and seasonal variations of meteor speed and arrival angle using Mengcheng meteor radar observations, providing insights into meteoroid dynamics in the mesosphere and lower thermosphere.

Recommended citation: Wu, K., Yi, W.*, Xue, X.*, Reid, I., & Lu, M. (2024). "Diurnal and seasonal variations of meteor speed and arrival angle observed by Mengcheng meteor radar." JGR: Space Physics.
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A Summary Report on the Space Physics Practical Education in 2022

Published in Review of Geophysics and Planetary Physics, 2024

A comprehensive report on space physics practical education initiatives in 2022, documenting educational programs and outcomes in space science education.

Recommended citation: Wu, K.*, Xu, X., Jiang, J., & Shen, A. (2024). "A Summary Report on the Space Physics Practical Education in 2022." Review of Geophysics and Planetary Physics.
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Preprints and Working Papers

From Personas to Simulated Users: A Fitness-for-Purpose Survey

Published in Submitted to AAAI 2027 Artificial Intelligence for Social Impact Track, 2027

A fitness-for-purpose survey of the progression from static personas to simulated users, submitted to the AAAI 2027 Artificial Intelligence for Social Impact Track.

Recommended citation: Liu, X., et al. (including Wu, K.) (2027). "From Personas to Simulated Users: A Fitness-for-Purpose Survey." Submitted to the AAAI 2027 Artificial Intelligence for Social Impact Track.

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

Published in arXiv preprint; submitted to AAAI 2027, 2026

A benchmark for end-to-end autonomous scientific research across 40 tasks from 10 scientific domains, with real-paper grounding and expert-curated multimodal rubrics.

Recommended citation: Xu, W., et al. (including Wu, K.) (2026). "ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research." arXiv preprint. Submitted to AAAI 2027.
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ESM-bench: A Benchmark for Evaluating Whether AI Agents Understand Earth System Model Physics and Code

Published in Preprint (Zenodo); in preparation for NeurIPS Datasets and Benchmarks, 2026

Preprint. A 243-task benchmark testing whether AI agents understand Earth System Model physics and code, with multi-model evaluation, a classification rubric, precision/recall/F1 scoring, and leakage detection. In preparation for NeurIPS Datasets and Benchmarks.

Recommended citation: Wu, K., Cao, Y., & Mai, G. (2026). "ESM-bench: A Benchmark for Evaluating Whether AI Agents Understand Earth System Model Physics and Code." Preprint, Zenodo. https://zenodo.org/records/19802836
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Noah-Agent: A Multi-Expert AI Agent Framework for Automated Parameterization and Validation of Large-Scale Fortran Climate Models (v0.1)

Published in Preprint (Zenodo); in preparation, 2025

Preprint. A multi-expert AI agent framework for automated parameterization and validation of large-scale Fortran climate models. Version 0.1, in preparation.

Recommended citation: Wu, K. (2025). "Noah-Agent: A Multi-Expert AI Agent Framework for Automated Parameterization and Validation of Large-Scale Fortran Climate Models (v0.1)." Preprint, Zenodo. https://zenodo.org/records/17862049
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Koutian Wu