arXiv:2512.20732v2 Announce Type: replace Abstract: As LLMs advance their reasoning capabilities about the physical world, the absence of rigorous benchmarks for evaluating their ability to generate scientifically valid physical models has become a critical gap. Computational mechanics, which develops and applies mathematical models and numerical methods to predict the behavior of physical systems under forces, deformation, and constraints, provides an ideal foundation for structured scientific reasoning evaluation. Problems follow clear mathematical structure, enforce strict physical and nume
As LLMs demonstrate increasing reasoning capabilities, the need for robust scientific benchmarks to assess their understanding of the physical world becomes critical.
This benchmark addresses a significant gap in evaluating code-generating LLMs, paving the way for more reliable and scientifically accurate AI applications in complex domains like computational mechanics.
The introduction of FEM-Bench provides a standardized and structured method to assess LLMs' ability to generate scientifically valid physical models, moving beyond general language understanding.
- · AI developers focused on scientific applications
- · Engineering and scientific research sectors
- · LLMs capable of advanced scientific reasoning
- · Companies seeking automated scientific model generation
- · LLMs with poor scientific reasoning capabilities
- · Traditional manual model development processes
Increased accuracy and reliability of AI-generated scientific models.
Acceleration of research and development in fields relying on complex physical simulations.
Potential for autonomous scientific discovery and problem-solving by advanced AI agents.
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Read at arXiv cs.LG