# World Models

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

Hierarchical Joint-Embedding Predictive Architectures (H-JEPA) previously planned by running gradient descent on actions at test time, and the iterative solver consumed 96.6%–97.7% of episode runtime on long-horizon tasks. WARP-JEPA keeps the world models frozen and replaces online optimization with learned multi-candidate trajectory proposers, scoring candidates with the frozen predictor inside the generation loop. Across 30 frozen two-, three- and four-level models on four benchmarks, it speeds long-horizon episode inference by 9.6–18.8× (2.9–3.0× on Cube) and raises task success by up to 33.3 percentage points.

Hierarchical Joint-Embedding Predictive Architectures (H-JEPA) previously planned by running gradient descent on actions at test time, and the iterative solver consumed 96.6%–97.7% of episode runtime on long-horizon tasks. WARP-JEPA keeps the world models frozen and replaces online optimization with learned multi-candidate trajectory proposers, scoring candidates with the frozen predictor inside the generation loop. Across 30 frozen two-, three- and four-level models on four benchmarks, it speeds long-horizon episode inference by 9.6–18.8× (2.9–3.0× on Cube) and raises task success by up to 33.3 percentage points.

NVIDIA has packaged world-foundation-model training, optimization and inference into Cosmos Framework, with agent-callable skills and checkpoints that run out of the box. Developers previously had to fit a model to their own sensors, action space and data, then get it into an application, two separate rework efforts. The released Cosmos 3 Nano simulation recipes cover driving, robotics and surgery: a Transfer-Auto reference run on four GPUs returns seven 720p videos and 99 LiDAR sweeps, while Sim-Auto, distilled into a causal few-step model, generates observations step by step from driving-policy input and returns them to the policy for closed-loop evaluation.

NVIDIA has packaged world-foundation-model training, optimization and inference into Cosmos Framework, with agent-callable skills and checkpoints that run out of the box. Developers previously had to fit a model to their own sensors, action space and data, then get it into an application, two separate rework efforts. The released Cosmos 3 Nano simulation recipes cover driving, robotics and surgery: a Transfer-Auto reference run on four GPUs returns seven 720p videos and 99 LiDAR sweeps, while Sim-Auto, distilled into a causal few-step model, generates observations step by step from driving-policy input and returns them to the policy for closed-loop evaluation.

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and tabletop blocks, the first controlled world in its FoundationWorlds program. It automatically generates tasks and demonstrations, letting researchers vary training data, repeat experiments and trace why a robot fails. In physical tests, the low-level skill model ManiFlow, trained only on simulation data, reached 80.0% success in simulation and 81.1% on the real robot, while the combined high-level planner plus low-level executor succeeded in 55.5% of simulated episodes and 50.0% of real-world episodes.

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and tabletop blocks, the first controlled world in its FoundationWorlds program. It automatically generates tasks and demonstrations, letting researchers vary training data, repeat experiments and trace why a robot fails. In physical tests, the low-level skill model ManiFlow, trained only on simulation data, reached 80.0% success in simulation and 81.1% on the real robot, while the combined high-level planner plus low-level executor succeeded in 55.5% of simulated episodes and 50.0% of real-world episodes.

AffectWorld evaluates world models with emotion treated as a causal variable driving future world change, and five state-of-the-art models reach only 0.100–0.280 on world-state change and 0.027–0.070 on counterfactual emotion sensitivity across 1,498 everyday videos.

AffectWorld evaluates world models with emotion treated as a causal variable driving future world change, and five state-of-the-art models reach only 0.100–0.280 on world-state change and 0.027–0.070 on counterfactual emotion sensitivity across 1,498 everyday videos.

The action-conditioned world model HF-WM trains on egocentric human manipulation video together with perturbed failure actions, reaching state-of-the-art action-conditioned video generation and predicted success rates that track real-world deployment.

The action-conditioned world model HF-WM trains on egocentric human manipulation video together with perturbed failure actions, reaching state-of-the-art action-conditioned video generation and predicted success rates that track real-world deployment.