Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters
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Computer Science > Artificial Intelligence
arXiv:2606.09774 (cs)
[Submitted on 8 Jun 2026 (v1), last revised 25 Jun 2026 (this version, v2)]
Title:Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters
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Abstract:Configuring an advanced scientific simulator, translating a modeling goal into a valid, runnable input deck, is a persistent bottleneck that costs domain scientists hours to days. Input decks are executable interfaces: simulator-specific vocabulary, cross-file references, schema constraints, and validation rules must align before a simulation can run. We show that this bottleneck can be substantially reduced with a lightweight adapter around an off-the-shelf coding agent, rather than a bespoke simulator agent. Coding agents already navigate files, edit code, run commands, and repair outputs; what they lack is the simulator's executable contract, and rebuilding the agent loop risks discarding harness-calibrated tool-use and self-correction behavior. We introduce SIGA, a coding-agent adapter that supplies this contract through retrieval, procedural memory, agent-callable validation, and validation-gated termination while leaving the model and loop frozen. Because this contract is small and external, SIGA also supports adapter self-evolution: prior trajectories can rewrite the adapter contents without modifying the underlying agent. On GEOS, a multiphysics subsurface simulator, SIGA's main gain is reliability: on harder held-out tasks it improves TreeSim from 0.720 to 0.789 and reduces across-run standard deviation by about 16x by preventing empty or invalid decks. In a human calibration, SIGA reaches in about five minutes the deck quality a domain expert reached in about three hours. Transfers to OpenFOAM and LAMMPS show the recipe is portable but interface-dependent: completion gates help when structural completeness is the bottleneck, while memory and retrieval help when value correctness is.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.09774 [cs.AI] |
| (or arXiv:2606.09774v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.09774
arXiv-issued DOI via DataCite
|
Submission history
From: Matthew Ho [view email][v1] Mon, 8 Jun 2026 17:35:17 UTC (3,240 KB)
[v2] Thu, 25 Jun 2026 18:32:21 UTC (1,474 KB)
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View a PDF of the paper titled Auto-Configuring Scientific Simulators with Lightweight Coding-Agent Adapters, by Matthew Ho and 4 other authors
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