Paper 2025/780

The Planted Orthogonal Vectors Problem

Abstract

In the $k$-Orthogonal Vectors ($k$-OV) problem we are given $k$ sets, each containing $n$ binary vectors of dimension $d=n^{o(1)}$, and our goal is to pick one vector from each set so that at each coordinate at least one vector has a zero. It is a central problem in fine-grained complexity, conjectured to require $n^{k-o(1)}$ time in the worst case. We propose a way to plant a solution among vectors with i.i.d. $$-biased entries, for appropriately chosen $$, so that the planted solution is the unique one. Our conjecture is that the resulting $$-OV instances still require time $$ to solve, on average. Our planted distribution has the property that any subset of strictly less than $$ vectors has the same marginal distribution as in the model distribution, consisting of i.i.d. $$-biased random vectors. We use this property to give average-case search-to-decision reductions for $$-OV.