  In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows
method to final states containing multiple neutrinos. The architecture can
natively scale for all combinations of object types and multiplicities in the
final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton
events, the momenta of both neutrinos and correlations between them are
reconstructed more accurately than when using the most popular standard
analytical techniques, and solutions are found for all events. Inference time
is significantly faster than competing methods, and can be reduced further by
evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to
$t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded
distributions is much closer to the limit of performance set by perfect
neutrino reconstruction than standard techniques. For the chosen double
differential observables $\nu^2$-Flows results in improved statistical
precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino
Weighting method and up to a factor of four in comparison to the Ellipse
approach.
