AI & Machine Learning · Thu, Nov 5, 2026

Machine learning, AI, natural language processing, computer vision and multi-agent systems.

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arXiv cs.AI · cs.CV · cs.LG Open resources

Interactive World Simulation with Cosmos for Physical AI

In briefReal-time interactive world simulation with Cosmos for training physical AI

SummaryThis paper presents a shared framework that adapts Cosmos world foundation models for real-time interactive simulation. It streams visual observations in response to actions and scene controls, letting users and embodied agents interact with evolving virtual environments. The approach is demonstrated on camera navigation, robot manipulation, controllable video transfer, and multi-view autonomous vehicle simulation, with code and model checkpoints released.

NVIDIA: Anis Ahmad, Jon Allen, Malik Aqeel Anwar et al.

arXiv cs.CV

Learning to Track Cells from Sparse Annotations in 3D Time-Lapse Microscopy

In briefLearning to track cells in 3D microscopy videos using sparse labels

SummaryThis paper presents a pipeline for tracking cells in 3D time-lapse microscopy, where only some cells are annotated, to reconstruct cell lineages. It detects cells from sparse annotations, calibrates candidate links using image features, and jointly selects lineage graph events. On 199 videos from 2 zebrafish embryos, it achieves an overall score of 0.9762 in 5-fold cross-validation.

Yusuke Uchida

★★★★☆ Notable
arXiv cs.LG · cs.DS

Dimension-Free Private Mean Estimation is Impossible Without the Covariance

In briefPrivate mean estimation without known covariance cannot avoid dimension dependence

SummaryThis paper studies the limits of differentially private Gaussian mean estimation when the covariance matrix is unknown. When the covariance is known, optimal sample complexity depends only on its spectrum rather than the ambient dimension. The authors prove that private estimators cannot achieve similar guarantees when the covariance is unknown. They give lower bounds showing error gaps of order √(d/n) when the sample size is below the effective dimension, and of order √d/n^{3/2} above it, even for diagonal covariances.

WhyThe authors prove dimension-dependent lower bounds for private Gaussian mean estimation with unknown covariance, contrasting the known-covariance case.

Renaud Gaucher, Ephraim Linder, Adam Smith et al.