PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design
Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whether an engineering design will work when executed. Existing benchmarks assess spatial reasoning, structural validity, or physics-grounded construction, but they do not determine whether MLLMs can synthesize complete load-bearing structures and repair them after simulator execution exposes a failure. We introduce PolyBridgeBench, an executable benchmark for multimodal bridge design. A model receives a visual scene and structured engineering constraints and generates a complete node--member--material topology. Deterministic legality checks g...
| Authors | Zicheng Zhao, Dongyin Chen, Rui Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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