We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction. We argue this is the correct pretraining objective for diagnostic belief state encoders: predicting in representation space captures what is surgically meaningful (instrument kinematics, tissue state) rather than texture artifacts. MedOS-JEPA replaces MedOS's CNN backbone with the JEPA encoder, enabling two-phase training: self-supervised pretraining on unlabelled surgical video, then supervised fine-tuning. All 37 unit tests pass in 13.53 s on an NVIDIA A100-SXM4-80GB.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction. We argue this is the correct pretraining objective for diagnostic belief state encoders: predicting in representation space captures what is surgically meaningful (instrument kinematics, tissue state) rather than texture artifacts. MedOS-JEPA replaces MedOS's CNN backbone with the JEPA encoder, enabling two-phase training: self-supervised pretraining on unlabelled surgical video, then supervised fine-tuning. All 37 unit tests pass in 13.53 s on an NVIDIA A100-SXM4-80GB.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction. We argue this is the correct pretraining objective for diagnostic belief state encoders: predicting in representation space captures what is surgically meaningful (instrument kinematics, tissue state) rather than texture artifacts. MedOS-JEPA replaces MedOS's CNN backbone with the JEPA encoder, enabling two-phase training: self-supervised pretraining on unlabelled surgical video, then supervised fine-tuning. All 37 unit tests pass in 13.53 s on an NVIDIA A100-SXM4-80GB.
We present MedOS-JEPA, an integration of the Motion-Content Joint Embedding Predictive Architecture (MC-JEPA) as the visual backbone of MedOS — a dual-process world model for clinical AI. MC-JEPA jointly learns optical flow and semantic content from surgical video via a shared ViT encoder, without pixel reconstruction. We argue this is the correct pretraining objective for diagnostic belief state encoders: predicting in representation space captures what is surgically meaningful (instrument kinematics, tissue state) rather than texture artifacts. MedOS-JEPA replaces MedOS's CNN backbone with the JEPA encoder, enabling two-phase training: self-supervised pretraining on unlabelled surgical video, then supervised fine-tuning. All 37 unit tests pass in 13.53 s on an NVIDIA A100-SXM4-80GB.
