Title: Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: circuit representation learning, circuit transformation, multiview learning

Abstract: Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast structural heterogeneity between views—such as an And-Inverter Graph (AIG) versus an XOR-Majority Graph (XMG)—poses a critical barrier to effective fusion, especially for self-supervised techniques like masked modeling. Naively applying such methods fails, as the cross-view context is perceived as noise. Our key insight is that \emph{functional alignment is a necessary precondition to unlock the power of multiview self-supervision}. We introduce \textbf{MixGate}, a framework built on a principled training curriculum that first teaches the model a shared, function-aware representation space via an Equivalence Alignment Loss. Only then do we introduce a multiview masked modeling objective, which can now leverage the aligned views as a rich, complementary signal. Extensive experiments, including a crucial ablation study, demonstrate that our alignment-first strategy transforms masked modeling from an ineffective technique into a powerful performance driver.