Reviews: Streamlining Variational Inference for Constraint Satisfaction Problems

The work proposes the use of streamlining in the context of survey inspired decimation algorithms---a main approach alongside stochastic local search---for effciiently finding solutions to large satisfiable random instances of the Boolean satisfiability (SAT) problem. The paper is well-written and easy to follow (although some hasty mistakes remain, see below). The proposed approach is shown to improve the state of the art (to some extent) in algorithms for solving random k-SAT instances, especially by showing that streamlining constraints allow for solving instances that a closer to the sat-unsat phase transition point than previously for different values of k. In terms of motivations, while I do find it of interest to develop algorithmic approach which allow for more efficiently finding solutions to the hardest random k-SAT instances, it would be beneficial if the authors would expand the introduction with more motivations for the work. In terms of contributions, the proposal consists essentially of combining previous proposed ideas to obtain further advances (which is of course ok, but slightly lowers the novelty aspects).