University of Jinan

A 123.5-million-parameter generative model, Didymos, was trained once without task labels on 409,297 intensive care stays from seven databases in five countries and answers prediction questions by sampling a patient's futures. On held-out stays it read 13 primary endpoints from those futures at AUROC 0.83–0.98 and ranked first on average among 20 predictors, including label-trained LightGBM and TabPFN-3.5. Its simulated treatment responses agreed with eight of nine observational contrasts, yet at two hospitals in Russia and China that contributed no training data it needed minutes of continued training on unlabelled local stays to match locally supervised models.

A 123.5-million-parameter generative model, Didymos, was trained once without task labels on 409,297 intensive care stays from seven databases in five countries and answers prediction questions by sampling a patient's futures. On held-out stays it read 13 primary endpoints from those futures at AUROC 0.83–0.98 and ranked first on average among 20 predictors, including label-trained LightGBM and TabPFN-3.5. Its simulated treatment responses agreed with eight of nine observational contrasts, yet at two hospitals in Russia and China that contributed no training data it needed minutes of continued training on unlabelled local stays to match locally supervised models.

The work proposes TSegAgent, which reformulates tooth instance segmentation and FDI labeling of intra-oral scanned 3D models as a zero-shot geometric reasoning problem: multi-view renderings with curvature heatmaps and the SAM3 text prompt "tooth" produce candidate masks, which are merged into face-level instance labels via IoU and containment relations, after which a geometry-aware vision-language agent performs non-tooth region identification, central incisor localization, full-arch classification, and error correction through multi-round conversation; it reports mIoU 93.37, TLA 96.40, TSA 96.76, TIR 97.46, and TIR=1 87.17 on Teeth3DS with 1200 3D tooth models, and mIoU 82.10, TLA 96.68, TSA 95.99, TIR 85.41, and TIR=1 51.

The work proposes TSegAgent, which reformulates tooth instance segmentation and FDI labeling of intra-oral scanned 3D models as a zero-shot geometric reasoning problem: multi-view renderings with curvature heatmaps and the SAM3 text prompt "tooth" produce candidate masks, which are merged into face-level instance labels via IoU and containment relations, after which a geometry-aware vision-language agent performs non-tooth region identification, central incisor localization, full-arch classification, and error correction through multi-round conversation; it reports mIoU 93.37, TLA 96.40, TSA 96.76, TIR 97.46, and TIR=1 87.17 on Teeth3DS with 1200 3D tooth models, and mIoU 82.10, TLA 96.68, TSA 95.99, TIR 85.41, and TIR=1 51.