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This paper presents a polynomial-time algorithm for learning the structure of a Gaussian graphical model from a single trajectory of Glauber dynamics, with a trajectory-length guarantee that does not depend on the mixing time.
This paper analyzes the global distributional behavior induced by iterative masked-token resampling in masked language models using Glauber dynamics. It introduces a rectangle test for incompatibility, establishes mixing time bounds, and empirically demonstrates phase transitions and metastable semantic basins.