LeWAM trains visual representation, action-conditioned latent dynamics, and a stochastic policy in a single stage, raising planning success from 28.6% to 89.7% on contact-rich tasks and from 10.9% to 46.2% on long-horizon tasks while planning 32.3 times faster.
Radiolucent foreign body aspiration in adults sits at soft-tissue and mucus density on CT, so it has been found only by slice-by-slice manual reading and is easily missed. AI-PACS instead learns a standard Gaussian manifold of non-FBA airway topology from 804 unlabelled CT scans, flags deviations from that manifold as anomalies, and maps them onto the S1–S10 bronchial segments. In a 35-patient human-AI subset, sensitivity rose from 18.8% for radiologists to 56.3%, and average negative-exclusion screening time fell by about 75%.
Evo-World adds optical-flow-supervised future motion prediction to a lightweight vision-language-action policy, reaching 91.1% tier-averaged success on Meta-World MT50, a 13.0% relative gain over the non-predictive baseline, and beating the evaluated baselines on AgileX single-arm and bimanual tasks with 0.87B parameters.
The work shows that isotropic Gaussian regularization (SIGReg) can make Euclidean latent planning cost rank feasible outcomes differently from the task cost, and introduces AnisoWM with ΛReg, which learns a diagonal covariance target under fixed-trace and anisotropy constraints while leaving the prediction objective and Euclidean planner unchanged, improving planning success over LeWM in all four visual control environments (TwoRoom, Reacher, PushT, Cube) and yielding better agreement between latent costs and task outcomes.
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