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M. Phung

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Preprint Aug 2026

Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control

A finite-sample learning-to-control theory for geometrically supervised latent models of nonlinear deterministic systems is established and an encoder-only local--global metric hinge is introduced that enforces directional resolution and separated-state discrimination.

Alain Bensoussan, M. Phung, Minh-Binh Tran · 0 citations