# SQUAD: Scalable Quorum Adaptive Decisions via ensemble of early exit neural networks

arXiv: https://arxiv.org/abs/2601.22711 · HTML: https://mlsanity.com/paper/2601.22711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [SNAS: Stochastic Neural Architecture Search](https://mlsanity.com/paper/1812.09926/) (2018)
- [Depth Uncertainty in Neural Networks](https://mlsanity.com/paper/2006.08437/) (2020)
- [Neural Ensemble Search for Uncertainty Estimation and Dataset Shift](https://mlsanity.com/paper/2006.08573/) (2020)
- [IDK Cascades: Fast Deep Learning by Learning not to Overthink](https://mlsanity.com/paper/1706.00885/) (2017)
- [One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking](https://mlsanity.com/paper/2104.00597/) (2021)
- [NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks](https://mlsanity.com/paper/2401.13330/) (2024)
- [Sub-Architecture Ensemble Pruning in Neural Architecture Search](https://mlsanity.com/paper/1910.00370/) (2019)
- [Neural Ensemble Search via Bayesian Sampling](https://mlsanity.com/paper/2109.02533/) (2021)
- [QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring](https://mlsanity.com/paper/2404.12599/) (2024)
- [DYNAMAX: Dynamic computing for Transformers and Mamba based architectures](https://mlsanity.com/paper/2504.20922/) (2025)

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

- [HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search](https://mlsanity.com/paper/2605.04103/) (2026)

## Abstract (arXiv)

Early-exit neural networks have become popular for reducing inference latency by allowing intermediate predictions when sufficient confidence is achieved. However, standard approaches typically rely on single-model confidence thresholds, which are frequently unreliable due to inherent calibration issues. To address this, we introduce SQUAD (Scalable Quorum Adaptive Decisions), the first inference scheme that integrates early-exit mechanisms with distributed ensemble learning, improving uncertainty estimation while reducing the inference time. Unlike traditional methods that depend on individual confidence scores, SQUAD employs a quorum-based stopping criterion on early-exit learners by collecting intermediate predictions incrementally in order of computational complexity until a consensus is reached and halting the computation at that exit if the consensus is statistically significant. To maximize the efficacy of this voting mechanism, we also introduce QUEST (Quorum Search Technique), a Neural Architecture Search method to select early-exit learners with optimized hierarchical diversity, ensuring learners are complementary at every intermediate layer. This consensus-driven approach yields statistically robust early exits, improving the test accuracy up to 5.95% compared to state-of-the-art dynamic solutions with a comparable computational cost and reducing the inference latency up to 70.60% compared to static ensembles while maintaining a good accuracy.