@inproceedings{polat-etal-2025-stress,
    title = "Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning",
    author = "Polat, Can  and
      Kurban, Hasan  and
      Serpedin, Erchin  and
      Kurban, Mustafa",
    editor = "Zhang, Yuji  and
      Chen, Canyu  and
      Li, Sha  and
      Geva, Mor  and
      Han, Chi  and
      Wang, Xiaozhi  and
      Feng, Shangbin  and
      Gao, Silin  and
      Augenstein, Isabelle  and
      Bansal, Mohit  and
      Li, Manling  and
      Ji, Heng",
    booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.knowllm-1.5/",
    pages = "49--58",
    ISBN = "979-8-89176-283-1",
    abstract = "Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces, xCrysAlloys, a multiscale multicrystal dataset with two physically grounded evaluation protocols to stress-test multimodal generative models. The Spatial-Exclusion benchmark withholds all supercells of a given radius from a diverse dataset, enabling controlled assessments of spatial interpolation and extrapolation. The Compositional-Exclusion benchmark omits all samples of a specific chemical composition, probing generalization across stoichiometries. Nine vision{--}language foundation models are prompted with crystallographic images and textual context to generate structural annotations. Responses are evaluated via (i) relative errors in lattice parameters and density, (ii) a physics-consistency index penalizing volumetric violations, and (iii) a hallucination score capturing geometric outliers and invalid space-group predictions. These benchmarks establish a reproducible, physically informed framework for assessing generalization, consistency, and reliability in large-scale multimodal models. Dataset and implementation are available at https://github.com/KurbanIntelligenceLab/StressTestingMMFMinCR."
}