# Task-Aware Discretization of Differentiable Logic Gate Networks

arXiv: https://arxiv.org/abs/2609.33747 · HTML: https://mlsanity.com/paper/2609.33747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Rethinking Architecture Selection in Differentiable NAS](https://mlsanity.com/paper/2108.04392/) (2021)
- [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)
- [Recurrent Deep Differentiable Logic Gate Networks](https://mlsanity.com/paper/2508.06097/) (2025)
- [Light Differentiable Logic Gate Networks](https://mlsanity.com/paper/2510.03250/) (2025)
- [LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment](https://mlsanity.com/paper/2511.12340/) (2025)
- [WARP Logic Neural Networks](https://mlsanity.com/paper/2602.03527/) (2026)
- [Align Forward, Adapt Backward: Closing the Discretization Gap in Logic Gate Networks](https://mlsanity.com/paper/2603.14157/) (2026)
- [Learning Compact Boolean Networks](https://mlsanity.com/paper/2602.05830/) (2026)

## Abstract (arXiv)

Differentiable logic gate networks (DLGNs) enable gradient-based training of highly efficient Boolean networks by relaxing discrete logic gates during training and discretizing them for inference. Standard approaches make this discretization decision locally, typically through argmax selection and confidence- or entropy-based convergence criteria. We show that local discretization can be task-suboptimal even for globally optimal relaxed solutions, with high gate confidence providing no general guarantee, and derive bounds relating task-aware gate selection to tractable interventions in the relaxed network. Motivated by these results, we study first-order downstream task information for progressive discretization and characterize when this local approximation is reliable. Experiments on convolutional DLGNs reveal a strong locality dependence: first-order scores become unreliable when directly optimized over nonlocal interventions, but accurately assess local argmax decisions for progressive freezing.