# Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning

arXiv: https://arxiv.org/abs/2509.20968 · HTML: https://mlsanity.com/paper/2509.20968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [DeepGate: Learning Neural Representations of Logic Gates](https://mlsanity.com/paper/2111.14616/) (2021)
- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks](https://mlsanity.com/paper/2303.08256/) (2023)
- [DeepGate3: Towards Scalable Circuit Representation Learning](https://mlsanity.com/paper/2407.11095/) (2024)
- [CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design](https://mlsanity.com/paper/2505.02168/) (2025)
- [The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models](https://mlsanity.com/paper/2403.07257/) (2024)
- [DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale](https://mlsanity.com/paper/2502.01681/) (2025)
- [DeepTPI: Test Point Insertion with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.06975/) (2022)
- [Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment](https://mlsanity.com/paper/2502.12732/) (2025)
- [Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering](https://mlsanity.com/paper/2404.05211/) (2024)

## Built on by (most cited first, 1 of 1)

- [GTAC: A Generative Transformer for Approximate Circuits](https://mlsanity.com/paper/2601.19906/) (2025)

## Abstract (arXiv)

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 functional alignment is a necessary precondition to unlock the power of multiview self-supervision. We introduce 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.