CariesBench: A Hierarchical Multi-view Benchmark for Dental Caries Screening and Diagnostic Reasoning from 3D Intraoral Scans

Dental caries assessment is a structured clinical process requiring lesion localization, identification of the affected tooth and surface, and severity grading rather than simple image-level recognition. 3D intraoral scans (IOS) preserve detailed dental geometry and surface morphology and support standardized rendering from complementary viewpoints, enabling fine-grained visual assessment while introducing a distinct need for anatomically grounded multi-view reasoning. Despite recent advances in multimodal large language models (MLLMs) and dental vision-language benchmarks, existing evaluations do not jointly assess hierarchical arch-to-tooth-to-surface reasoning, cross-view evidence integration, and surface-level caries assessment. To address this gap, we introduce CariesBench, a hierarchical benchmark constructed from 5,932 3D IOS, comprising 85,455 standardized multi-view renderings and 133,994 clinically grounded visual question answering instances with expert quality control. CariesBench comprises three complementary task families: Arch-level Clinical Screening (ACS), Tooth-level Diagnostic Reasoning (TDR), and Structured Clinical Decision-Making (SCD). Using the CariesBench training set, we further fine-tune Qwen3-VL-8B-Instruct to obtain IntraoralGPT. Extensive evaluation of 24 representative MLLMs reveals a pronounced global-to-local reasoning gap, with substantially stronger performance on arch-level screening than on fine-grained tooth- and surface-level reasoning. CariesBench-specific supervision substantially improves the Qwen3-VL-8B-Instruct baseline, increasing the 19-task macro-average clinical accuracy from 52.7% to 69.5% and TDR Overall from 34.4% to 44.7%. Despite these gains, fine-grained TDR remains challenging, highlighting persistent difficulty in anatomical localization and cross-view evidence integration. CariesBench thus provides a clinically grounded testbed for evaluating hierarchical multi-view reasoning in digital dentistry.