arXiv:2607.00436v1 Announce Type: new Abstract: Large language model agents are increasingly connected to scientific software, yet it remains unclear when tool access makes scientific computation more reliable rather than merely more complex. We introduce PHREEQC-MCQ-200, a benchmark for evaluating tool-augmented agents on deterministic aqueous-geochemistry simulations. The benchmark contains 200 multiple-choice questions derived from 21 validated PHREEQC scenarios, requiring agents to construct simulator inputs, execute PHREEQC, inspect structured outputs, and commit to final answers. Across
The proliferation of large language model agents in scientific applications necessitates robust methods for evaluating their reliability and utility, moving beyond mere complexity.
This benchmark helps to standardize the evaluation of tool-augmented AI agents in scientific simulation, critical for validating their practical deployment and improving scientific discovery processes.
The introduction of a specific, validated benchmark for aqueous-geochemistry simulations provides a clearer path for developing and assessing agents that can reliably interact with scientific software.
- · AI agent developers
- · Scientific research institutions
- · Geochemistry researchers
- · Anecdotal AI agent evaluation methods
- · Developers of unreliable scientific AI tools
Improved reliability and broader adoption of AI agents in complex scientific simulation tasks.
Accelerated discovery of new materials or environmental solutions due to more efficient and accurate simulations.
Reduced need for human experts in certain routine scientific simulation and analysis, shifting roles towards oversight and novel problem framing.
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Read at arXiv cs.AI