aiqbench analysis
This research introduces a "render ceiling" benchmark that isolates genuine reasoning errors from visual misinterpretations in vision-language models by using model-free rendering of crystal structures. By proving that all deficits across fourteen tested models stem from the models themselves rather than flawed benchmarks, the study provides a rigorous standard for evaluating multimodal AI capabilities in scientific domains. The findings also demonstrate that supervised vision models without language components currently outperform many multimodal systems on these specific geometry tasks.
Separating perception from reasoning in vision-language models: a model-free render ceiling for crystal structures
arXiv:2609.00663v1 Announce Type: cross Abstract: Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the frozen cameras and re-solving cross-view correspondence recovers exactly the answer the images support. We prove the ceiling fails only through an enumerable set of projection coincidences and certify that set empty on 2,160 rendered crystal structures, so every point of a model's deficit belongs to the model. Across fourteen vision-language models, supplying exact geometry as text lifts every model yet closes under half the gap for thirteen, while a supervised vision model with no language component reads the same images at 0.8952, above every vision-language model. The instrument exposes extraction-stage fabrication that downstream accuracy would misattribute to reasoning, yields camera-placement rules for benchmark builders, and transfers to any benchmark with an invertible forward rendering.
Abstract:Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the frozen cameras and re-solving cross-view correspondence recovers exactly the answer the images support. We prove the ceiling fails only through an enumerable set of projection coincidences and certify that set empty on 2,160 rendered crystal structures, so every point of a model's deficit belongs to the model. Across fourteen vision-language models, supplying exact geometry as text lifts every model yet closes under half the gap for thirteen, while a supervised vision model with no language component reads the same images at 0.8952, above every vision-language model. The instrument exposes extraction-stage fabrication that downstream accuracy would misattribute to reasoning, yields camera-placement rules for benchmark builders, and transfers to any benchmark with an invertible forward rendering.
Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci); Chemical Physics (physics.chem-ph); Quantum Physics (quant-ph)
arXiv:2609.00663 [cs.CV]
(or arXiv:2609.00663v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.00663 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 191 words
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