I'd like to highlight one of the new FOCS papers "New Limits to Classical and Quantum Instance Compression" by MIT student Andrew Drucker which solves a problem that has dogged me for a few years. Ignore the "Quantum" in the title, that's not the interesting part.

Consider the OR-SAT problem, given m formulas each of size n, is at least one of them satisfiable. In STOC 2008, Rahul Santhanam and I showed that if there is a reduction from OR-SAT to any language L with the property that the reduction reduces to instances of size polynomial in n (independent of m) then the polynomial-time hierarchy collapses. The question arose from parameterized complexity though it also has some applications to cryptography and PCPs.

Our techniques failed to get a similar result for AND-SAT, where we want to know if all of the formulas are satisfiable. Drucker settled the question, showing that a compression of AND-SAT (reducing to instances of size polynomial in n) also gives a similar collapse of the hierarchy. What impressed me the most was the ingenuity Drucker uses in his proof.

Drucker derives the following lemma about probability distributions based on a strong version of Fano's inequality. Let A be a set of strings, D a distribution over A and f any function that maps A

^{m}to {0,1}

^{m-2}. If you pick a y from D, with high probability there is an index i, such that the distributions f(D

^{m}) and f(D

^{m}) with the ith location replaced by y are statistically close.

Drucker combines this lemma with a minimax argument and a result of Sahai and Vadhan that characterizes Statistical Zero-Knowledge as determining whether two distributions are different from the circuits that generate them.

Sometimes people solve your open questions with a simple argument that makes you hit your head for not thinking of it yourself. Other times you just sit back amazed at the solution knowing there was no hope for you to find it. Drucker's paper definitely falls in that second category.