# Goal-oriented Communications based on Recursive Early Exit Neural Networks

arXiv: https://arxiv.org/abs/2412.19587 · HTML: https://mlsanity.com/paper/2412.19587/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Placed in

- Efficient LLM inference and serving / [Early exit and edge inference](https://mlsanity.com/inference/early-exit-and-edge-inference/)

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [BERT Loses Patience: Fast and Robust Inference with Early Exit](https://mlsanity.com/paper/2006.04152/) (2020)
- [Zero Time Waste: Recycling Predictions in Early Exit Neural Networks](https://mlsanity.com/paper/2106.05409/) (2021)
- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization](https://mlsanity.com/paper/2310.13948/) (2023)
- [Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting](https://mlsanity.com/paper/2312.03555/) (2023)
- [Age of Loop for Wireless Networked Control Systems Optimization](https://mlsanity.com/paper/2106.00415/) (2021)
- [Early-Exit meets Model-Distributed Inference at Edge Networks](https://mlsanity.com/paper/2408.05247/) (2024)
- [Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels](https://mlsanity.com/paper/2311.18098/) (2023)
- [Sustainable Edge Intelligence Through Energy-Aware Early Exiting](https://mlsanity.com/paper/2305.14094/) (2023)
- [6G goal-oriented communications: How to coexist with legacy systems?](https://mlsanity.com/paper/2308.13358/) (2023)

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

- [Adaptive Semantic Token Communication for Transformer-based Edge Inference](https://mlsanity.com/paper/2505.17604/) (2025)

## Abstract (arXiv)

This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First, we introduce an innovative early exit strategy that dynamically partitions computations, enabling samples to be offloaded to a server based on layer-wise recursive prediction dynamics that detect samples for which the confidence is not increasing fast enough over layers. Second, we develop a Reinforcement Learning-based online optimization framework that jointly determines early exit points, computation splitting, and offloading strategies, while accounting for wireless conditions, inference accuracy, and resource costs. Numerical evaluations in an edge inference scenario demonstrate the method's adaptability and effectiveness in striking an excellent trade-off between performance, latency, and resource efficiency.