  We revisit the approximate Voronoi cells approach for solving the closest
vector problem with preprocessing (CVPP) on high-dimensional lattices, and
settle the open problem of Doulgerakis-Laarhoven-De Weger [PQCrypto, 2019] of
determining exact asymptotics on the volume of these Voronoi cells under the
Gaussian heuristic. As a result, we obtain improved upper bounds on the time
complexity of the randomized iterative slicer when using less than $2^{0.076d +
o(d)}$ memory, and we show how to obtain time-memory trade-offs even when using
less than $2^{0.048d + o(d)}$ memory. We also settle the open problem of
obtaining a continuous trade-off between the size of the advice and the query
time complexity, as the time complexity with subexponential advice in our
approach scales as $d^{d/2 + o(d)}$, matching worst-case enumeration bounds,
and achieving the same asymptotic scaling as average-case enumeration
algorithms for the closest vector problem.
