Koutian Wu

Koutian Wu

Researcher and founder working on AI for Science. M.S. @ UT-Austin. ex-Undergrad @ USTC.

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publications

Perturbations by the 2022 Hunga-Tonga Volcano Eruption in the MLT Region Investigated Using the WACCM-X Simulation and Meteor Radar Observations

Published in AGU Fall Meeting 2023, 2023

AGU Fall Meeting abstract studying wave perturbations from the 2022 Hunga-Tonga eruption in the mesosphere and lower thermosphere using WACCM-X simulations and meteor radar observations.

Recommended citation: Wu, K., Liu, H.-L., Yi, W., & Xue, X. (2023). "Perturbations by the 2022 Hunga-Tonga Volcano Eruption in the MLT Region Investigated Using the WACCM-X Simulation and Meteor Radar Observations." AGU Fall Meeting Abstracts, SA33B-2892.
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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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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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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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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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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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How Does Integrating Plant Hydraulics Improve Noah-MP Land Surface Model

Published in 106th AMS Annual Meeting, 2026

Conference poster presenting the integration and evaluation of a plant hydraulics scheme in the Noah-MP land surface model.

Recommended citation: Wu, K., Li, L., Rempe, D., Matheny, A., Mbarak, M., & Yang, Z.-L. (2026). "How Does Integrating Plant Hydraulics Improve Noah-MP Land Surface Model." Poster presented at the 106th AMS Annual Meeting, Houston, TX.

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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MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Published in arXiv preprint arXiv:2608.04205, 2026

A population-scale simulated-user evaluation infrastructure with 8.3 billion persona records, four interactive playground environments, and 1,010 application tasks across 25+ domains.

Recommended citation: Li, X., et al. (including Wu, K.) (2026). "MatrAIx: Simulating the World with 8.3 Billion Persona Agents." arXiv preprint arXiv:2608.04205.
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MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations

Published in arXiv preprint arXiv:2608.15844, 2026

A behavioral-science instrument that measures identity drift in generative agents carrying an immutable “soul file” through a resource-scarce, long-horizon multi-agent simulation.

Recommended citation: Ng, S., et al. (including Wu, K.) (2026). "MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations." arXiv preprint arXiv:2608.15844.
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ASI-Bench: At the Dawn of Artificial Superintelligence

Published in arXiv preprint arXiv:2608.17271, 2026

A benchmark with 60 project-level tasks across 11 scientific domains that evaluates frontier models on open-ended scientific research beyond human-expert benchmarks.

Recommended citation: Zhou, J., et al. (including Wu, K.) (2026). "ASI-Bench: At the Dawn of Artificial Superintelligence." arXiv preprint arXiv:2608.17271.
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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.

talks

teaching

Earth in 2100 (GEO 303E)

Graduate Teaching Assistant, University of Texas at Austin, Jackson School of Geosciences, 2024

I served as a Graduate Teaching Assistant for Earth in 2100 (GEO 303E) in Fall 2024 and Spring 2025. The two course sections enrolled 1,013 students in total (522 in Fall and 491 in Spring).

GEO 302C

Graduate Teaching Assistant, University of Texas at Austin, Jackson School of Geosciences, 2026

I served as a Graduate Teaching Assistant for GEO 302C during Spring 2026. This appointment continued my undergraduate teaching service in the Jackson School of Geosciences after two semesters supporting Earth in 2100 (GEO 303E).