Language-Model Agents Running Verified Subsurface Multiphysics Simulations: Traced Numbers and the Modeling Choices That Still Need Checks

Language-model agents can now set up and run numerical simulations from a sentence, but a reported number can be wrong in two ways: the simulator never produced it, or it came from a model that was set up badly. We built GeoAI-THM Studio, an agent harness for OpenGeoSys 6.5.8 in which the language model edits only a typed model tree through 21 tools, a configuration is computed only after its verification cases pass (27 cases; 21 within 1 % of their exact solutions, official benchmarks or consistency checks, all within 2.5 %), and an output guardrail rejects answers whose checked numbers cannot be traced to the run, the model inputs or the request. In 16 runs with gpt-6-luna and gpt-6.1-terra on eight requests in heat, groundwater flow, deformation and solute transport, every number the guardrail checks in every answer was traced in this way, and Claude Haiku 5.5, standing in for a small model during development, passed 20 of 21 test scenarios, from which the eight requests were drawn. Yet both OpenAI models answered how much a 300 W point heat source raises pore pressure wrongly, −0.507 and 0.635 MPa against 0.967 MPa from the exact solution, because both had coarsened the mesh at the source, and gpt-6-luna also around it. gpt-6.1-terra had itself found that halving the elements changed its value by 0.257 MPa and called the result mesh-sensitive, but still reported it, and the scoring script, which checks procedure and provenance, passed both answers. Recomputing every answer on the mesh of its starting example exposed the errors. A sweep of the element size at the source showed the pressure within 3 % of the exact value only when the elements there are no larger than one eighth of the distance heat has spread by the first output, and the released harness refuses coarser meshes. We propose, as a hypothesis for further tests, that each modeling choice left to the language model needs a physics-based check of this kind.

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Publication Details

Published
2026-11-06
DOI
https://doi.org/10.22541/essoar.15011821/v1
Primary Topic
Artificial Intelligence Applications
Type
preprint
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preprint

Language-Model Agents Running Verified Subsurface Multiphysics Simulations: Traced Numbers and the Modeling Choices That Still Need Checks

Eungyu Park, Jangwon Park, Taeyu Kim
3 links
Artificial Intelligence Applications
preprint

Language-Model Agents Running Verified Subsurface Multiphysics Simulations: Traced Numbers and the Modeling Choices That Still Need Checks

Eungyu Park, Jangwon Park, Taeyu Kim
preprint en

Abstract

Language-model agents can now set up and run numerical simulations from a sentence, but a reported number can be wrong in two ways: the simulator never produced it, or it came from a model that was set up badly. We built GeoAI-THM Studio, an agent harness for OpenGeoSys 6.5.8 in which the language model edits only a typed model tree through 21 tools, a configuration is computed only after its verification cases pass (27 cases; 21 within 1 % of their exact solutions, official benchmarks or consistency checks, all within 2.5 %), and an output guardrail rejects answers whose checked numbers cannot be traced to the run, the model inputs or the request. In 16 runs with gpt-6-luna and gpt-6.1-terra on eight requests in heat, groundwater flow, deformation and solute transport, every number the guardrail checks in every answer was traced in this way, and Claude Haiku 5.5, standing in for a small model during development, passed 20 of 21 test scenarios, from which the eight requests were drawn. Yet both OpenAI models answered how much a 300 W point heat source raises pore pressure wrongly, −0.507 and 0.635 MPa against 0.967 MPa from the exact solution, because both had coarsened the mesh at the source, and gpt-6-luna also around it. gpt-6.1-terra had itself found that halving the elements changed its value by 0.257 MPa and called the result mesh-sensitive, but still reported it, and the scoring script, which checks procedure and provenance, passed both answers. Recomputing every answer on the mesh of its starting example exposed the errors. A sweep of the element size at the source showed the pressure within 3 % of the exact value only when the elements there are no larger than one eighth of the distance heat has spread by the first output, and the released harness refuses coarser meshes. We propose, as a hypothesis for further tests, that each modeling choice left to the language model needs a physics-based check of this kind.

Artificial Intelligence Applications
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Language-Model Agents Running Verified Subsurface Multiphysics Simulations: Traced Numbers and the Modeling Choices That Still Need Checks — Eungyu Park, Jangwon Park, et al. · (2026) | TGRS Research Map | TGRS