# Scalable Interconnect Learning in Boolean Networks

arXiv: https://arxiv.org/abs/2507.02585 · HTML: https://mlsanity.com/paper/2507.02585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Operation-Aware Soft Channel Pruning using Differentiable Masks](https://mlsanity.com/paper/2007.03938/) (2020)
- [Deep Differentiable Logic Gate Networks](https://mlsanity.com/paper/2210.08277/) (2022)
- [Convolutional Differentiable Logic Gate Networks](https://mlsanity.com/paper/2411.04732/) (2024)
- [Differentiable Weightless Neural Networks](https://mlsanity.com/paper/2410.11112/) (2024)
- [Learning Interpretable Differentiable Logic Networks](https://mlsanity.com/paper/2407.04168/) (2024)
- [Learning Symbolic Expressions via Gumbel-Max Equation Learner Networks](https://mlsanity.com/paper/2012.06921/) (2020)
- [Logic Gate Neural Networks are Good for Verification](https://mlsanity.com/paper/2505.19932/) (2025)
- [eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks](https://mlsanity.com/paper/2503.09910/) (2025)

## Built on by (most cited first, 4 of 4)

- [WARP-LUTs -- Walsh-Assisted Relaxation for Probabilistic Look Up Tables](https://mlsanity.com/paper/2510.15655/) (2025)
- [WARP Logic Neural Networks](https://mlsanity.com/paper/2602.03527/) (2026)
- [Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control](https://mlsanity.com/paper/2512.01467/) (2025)
- [BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks](https://mlsanity.com/paper/2602.07400/) (2026)

## Abstract (arXiv)

Learned Differentiable Boolean Logic Networks (DBNs) already deliver efficient inference on resource-constrained hardware. We extend them with a trainable, differentiable interconnect whose parameter count remains constant as input width grows, allowing DBNs to scale to far wider layers than earlier learnable-interconnect designs while preserving their advantageous accuracy. To further reduce model size, we propose two complementary pruning stages: an SAT-based logic equivalence pass that removes redundant gates without affecting performance, and a similarity-based, data-driven pass that outperforms a magnitude-style greedy baseline and offers a superior compression-accuracy trade-off.