# Recurrent Deep Differentiable Logic Gate Networks

arXiv: https://arxiv.org/abs/2508.06097 · HTML: https://mlsanity.com/paper/2508.06097/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [The State of Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1902.09574/) (2019)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Resurrecting Recurrent Neural Networks for Long Sequences](https://mlsanity.com/paper/2303.06349/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://mlsanity.com/paper/2402.14905/) (2024)
- [Efficient Machine Translation with a BiLSTM-Attention Approach](https://mlsanity.com/paper/2410.22335/) (2024)

## Built on by (most cited first, 12 of 12)

- [Light Differentiable Logic Gate Networks](https://mlsanity.com/paper/2510.03250/) (2025)
- [Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks](https://mlsanity.com/paper/2603.00302/) (2026)
- [From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks](https://mlsanity.com/paper/2509.25933/) (2025)
- [Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control](https://mlsanity.com/paper/2512.01467/) (2025)
- [Inter-patient ECG Arrhythmia Classification with LGNs and LUTNs](https://mlsanity.com/paper/2601.11433/) (2026)
- [Learning Compact Boolean Networks](https://mlsanity.com/paper/2602.05830/) (2026)
- [BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks](https://mlsanity.com/paper/2602.07400/) (2026)
- [GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression](https://mlsanity.com/paper/2601.14130/) (2026)
- [On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks](https://mlsanity.com/paper/2605.24649/) (2026)
- [Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices](https://mlsanity.com/paper/2607.18149/) (2026)
- [On the Depth Scalability of Logic Gate Networks](https://mlsanity.com/paper/2607.21633/) (2026)
- [Task-Aware Discretization of Differentiable Logic Gate Networks](https://mlsanity.com/paper/2609.33747/) (2026)

## Abstract (arXiv)

While differentiable logic gates have shown promise in feedforward networks, their application to sequential modeling remains unexplored. This paper presents the first implementation of Recurrent Deep Differentiable Logic Gate Networks (RDDLGN), combining Boolean operations with recurrent architectures for sequence-to-sequence learning. Evaluated on WMT'14 English-German translation, RDDLGN achieves 5.00 BLEU and 30.9% accuracy during training, approaching GRU performance (5.41 BLEU) and graceful degradation (4.39 BLEU) during inference. This work establishes recurrent logic-based neural computation as viable, opening research directions for FPGA acceleration in sequential modeling and other recursive network architectures.