An $\Omega ( (\log n / \log \log n)^2 )$ Cell-Probe Lower Bound for Dynamic Boolean Data Structures

TL;DR

This paper establishes a tight lower bound for dynamic Boolean data structures by introducing a novel communication game that incorporates verification, surpassing previous methodological barriers and likely representing a fundamental limit of current frameworks.

Contribution

The authors introduce a 2.5-round communication model with verification, enabling the proof of an optimal lower bound for dynamic Boolean data structures, surpassing prior barriers.

Findings

Proves an $oldsymbol{ ilde{oldsymbol{ ext{Omega}}}(( ext{log} n / ext{log} ext{log} n)^2)}$ lower bound.

Introduces a 2.5-round communication game with verification to bypass previous barriers.

Extends the approach to other hard Boolean functions, providing a general lower bound technique.

Abstract

We resolve the long-standing open problem of Boolean dynamic data structure hardness, proving an unconditional lower bound of $\Omega((\log n / \log\log n)^2)$ for the Multiphase Problem of Patrascu [STOC 2010] (instantiated with Inner Product over $\mathbb{F}_2$). This matches the celebrated barrier for weighted problems established by Larsen [STOC 2012] and closes the gap left by the $\Omega(\log^{1.5} n)$ Boolean bound of Larsen, Weinstein, and Yu [STOC 2018]. The previous barrier was methodological: all prior works relied on ``one-way'' communication games, where the inability to verify query simulations necessitated complex machinery (such as the Peak-to-Average Lemma) that hit a hard ceiling at $\log^{1.5} n$. Our key contribution is conceptual: We introduce a 2.5-round Multiphase Communication Game that augments the standard one-way model with a verification round, where Bob confirms the consistency of Alice's simulation against the actual memory. This simple, qualitative change allows us to bypass technical barriers and obtain the optimal bound directly. As a consequence, our analysis naturally extends to other hard Boolean functions, offering a general recipe for translating discrepancy lower bounds into $\Omega((\log n / \log\log n)^2)$ dynamic Boolean data structure lower bounds. We also argue that this result likely represents the structural ceiling of the Chronogram framework initiated by Fredman and Saks [STOC 1989]: any $\omega(\log^2 n)$ lower bound would require either fundamentally new techniques or major circuit complexity breakthroughs.