Author

Michael Muehlebach

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#artificial intelligence Preprint Oct 2026

Learning Structured Sparsity for Large Models: Bridging Model Compressibility and Efficient Inference

Scaling laws continue to reward increases in model scale, while the resulting computational and memory demands drive greater reliance on cloud infrastructure for inference. Yet unreliable network connectivity, communication delays, and privacy requirements can make such reliance impractical, motivating local execution...

Tian-Xiang Xia, Li-On Kai, Niao He et al. · 0 citations

Preprint Sep 2026

Co-VLA: Consensus-based Federated Training for Vision-Language-Action Models

Vision-language-action models (VLAs) have emerged as a promising paradigm for general-purpose robot learning, with performance improving as models and datasets scale. Scaling robot data collection, however, remains challenging because data are naturally distributed across robots, tasks, and locations, making centraliza...

Hao-Long Li, Güner Dilsad Er, Michael Muehlebach et al. · 0 citations