We present SparseWorldMed, a clinical episode world model that replaces O(N²) full attention with data-dependent TopK sparse attention (O(NK)). Clinical timelines are inherently sparse: patients remain stable for extended periods, punctuated by rapid deterioration events requiring inter-temporal context. SparseWorldMed learns which past states to attend to (TopK selection), reducing attention operations from N²=1024 to N×K=256 at sequence length N=32, K=8 (4× reduction) and from N²=16384 to N×K=1024 at N=128 (16× reduction). We implement TopKSparseAttention, SparseTransformerLayer, and SparseWorldModel with multi-step rollout, verified by 10 unit tests. The sparse world model integrates directly as a drop-in replacement for MedOS's ClinicalWorldModel, enabling long-horizon clinical episode simulation.
V-JEPA (Bardes et al. 2024) is integrated as the visual backbone of MedOS, a dual-process surgical world model. V-JEPA processes T-frame video clips with aggressive spatiotemporal masking: the context encoder sees only 25% of all N = T × H_p × W_p patches, while the predictor reconstructs 40% target patches via MSE in latent space. An EMA target encoder (momentum=0.996) provides stable regression targets. This replaces the 4-objective MC-JEPA loss (photometric + smoothness + backward + VICReg) with a single MSE objective and shifts temporal scale from 2-frame pairs (33ms) to T-frame clips (seconds). All 57 tests pass (37 original + 20 new V-JEPA tests). A mini model (32px, 4-frame, embed_dim=64) achieves VJEPA loss=1.2909 and confirmed output shapes robot_waypoints=(2,3,6). V-JEPA captures procedure-level temporal dependencies that 2-frame MC-JEPA misses.
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.
