Sample-Smooth Spaces: A Convenient Category for Differentiable Probabilistic Programming

We introduce the category SSS of sample-smooth spaces over a mixed site. The test objects are the products Ω n:= R n times Ω of a Cartesian space with the universal Hilbert cube Ω carrying all universally measurable sets, and a space is a set with a family of admissible plots Ω n to X closed under precomposition. Smoothness and measurability are then not two structures glued along an axiom, but one structure over one site. The site has finite non-empty products, because Ω absorbs its own square; its Karoubi envelope contains every R n; and it has mixed morphisms ω (W(ω),Φ(ω)), which turn measurability of a smooth family from an axiom into a consequence. SSS is a concrete quasitopos: complete, cocomplete, cartesian closed and locally cartesian closed, with a classifier for embeddings. Morphisms of Cartesian spaces are exactly the C infinity maps and manifolds embed full and faithfully, both without Boman's theorem. Every object has tangent and cotangent spaces, every morphism a differential. The modalities sit in an adjoint string Π Λ, making SSS cohesive over quasi-universal spaces. The point is the probability monad. Defining the plots of (X) as push-forwards of X-plots at every test object, is an unconditional strong commutative affine monad on all of SSS -- functor, unit, product of kernels, multiplication and the monad laws are each one line of seed splitting -- and its Kleisli category, of differentiable simulators, is a Markov category. The reparametrisation trick holds by construction: every Kleisli morphism is plot-wise a sampler, stably under composition. A reflection theorem locates the whole gain in a single plot family.