Non-Equilibrium Model Selection via Finite-Time Thermodynamics

Masayuki Ohzeki

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arXiv · 2606.16399

Non-Equilibrium Model Selection via Finite-Time Thermodynamics

Masayuki Ohzeki

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Abstract

Information criteria such as WAIC and WBIC extend model selection to singular learning machines, but they are usually derived for equilibrium posteriors. We formulate a finite-time analogue of WBIC by replacing the equilibrium posterior with an effective ensemble generated by learning dynamics under a resource constraint. When this ensemble admits an analytic effective potential, singular learning theory yields a resource-dependent real log canonical threshold. The resulting estimator gives a computable thermodynamic contribution to time-bounded MDL and identifies the finite-time singular complexity relevant to the structural information measured by epiplexity.

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BibTeX RIS

Masayuki Ohzeki. 2026-06-15. Non-Equilibrium Model Selection via Finite-Time Thermodynamics. https://arxiv.org/abs/2606.16399

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