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This paper introduces a general and principled construction of model spacepriors with a focus on regression problems. The proposed formulation regardseach model as a `local` null hypothesis whose alternatives are the set ofmodels that nest it. Assuming constant odds between any `local` null and itsalternatives provides a natural isomorphism of model spaces (like a matryoshkadoll), constituting an intuitive way to correct for test multiplicity. Thisisomorphism yields the Poisson distribution as the unique limiting distributionover model dimension under mild assumptions. We compare this model space priortheoretically and in simulations to widely adopted Beta-Binomial constructions.We show that the proposed prior yields a `just-right` multiplicity correctionthat induces a desirable complexity penalization profile.

The matryoshka doll prior: principled multiplicity correction in Bayesian model comparison