Adaptive Black-Box Exactness Barriers for Nearest-Source Girth Estimation in CONGEST

Abstract

Recent multi-scale nearest-source methods give polynomially sublinear approximations for girth in the CONGEST model. We study exactification by adaptive black-box composition while preserving the same fresh exchangeable source-selection primitive. Our scalar-oracle model exposes the sampled source identities and the scalar estimate from every call, allows arbitrary persistent controller state, adaptive source cardinalities and capacities, adaptive stopping, and an arbitrary final decoder; the internal nearest-source tables remain encapsulated. We first construct, for infinitely many $n$, a same-size pair $G_t^0,G_t^1$ of maximum-degree-three, logarithmic-diameter graphs whose girths are distinct and both $\Theta(\log n)$. A length-transfer construction makes every bulk source contribute identically on the two graphs. The scalar transcripts can separate the pair only when one of $O(\log n)$ interface sources survives a linear nearest-source rank competition. Coupling the adaptive executions with a conditional permutation-rank bound yields an $\Omega(n/\log n)$ expected retained-source workload requirement for constant exactness probability, even with arbitrary final decoding. For the standard sequential packetized realization, the same scale is an expected-round barrier. A complementary bridgeless family $\widehat H_t$ shows the same $\Omega(n/\log n)$ direct-retuning barrier on bounded-degree graphs with minimum degree at least two, no bridges, and $2$-core equal to the whole graph. Finally, under a known promise $g\ge h$, one full-source call from $\Theta(n/h)$ uniformly sampled sources computes exact girth with constant probability in $O(n/h+D)$ rounds, matching the $n/g$ source scale.