# Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

arXiv: https://arxiv.org/abs/2402.07573 · HTML: https://mlsanity.com/paper/2402.07573/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2024.

## Built on by (most cited first, 30 of 39)

- [Adaptive Semantic Token Communication for Transformer-based Edge Inference](https://mlsanity.com/paper/2505.17604/) (2025)
- [Dynamic Relative Representations for Goal-Oriented Semantic Communications](https://mlsanity.com/paper/2403.16986/) (2024)
- [Conditional computation in neural networks: principles and research trends](https://mlsanity.com/paper/2403.07965/) (2024)
- [Adaptive Semantic Token Selection for AI-native Goal-oriented Communications](https://mlsanity.com/paper/2405.02330/) (2024)
- [Causal Abstraction Learning based on the Semantic Embedding Principle](https://mlsanity.com/paper/2502.00407/) (2025)
- [Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks](https://mlsanity.com/paper/2504.00233/) (2025)
- [ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access](https://mlsanity.com/paper/2505.10946/) (2025)
- [Semantic Communication Enhanced by Knowledge Graph Representation Learning](https://mlsanity.com/paper/2407.19338/) (2024)
- [Latent Space Alignment for AI-Native MIMO Semantic Communications](https://mlsanity.com/paper/2507.16680/) (2025)
- [Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions](https://mlsanity.com/paper/2407.00081/) (2024)
- [Topological Dictionary Learning](https://mlsanity.com/paper/2503.11470/) (2025)
- [Learning the Structure of Connection Graphs](https://mlsanity.com/paper/2510.11245/) (2025)
- [Learning Network Sheaves for AI-native Semantic Communication](https://mlsanity.com/paper/2512.03248/) (2025)
- [Over-the-Air Semantic Alignment with Stacked Intelligent Metasurfaces](https://mlsanity.com/paper/2512.05657/) (2025)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
- [Soft Partitioning of Latent Space for Semantic Channel Equalization](https://mlsanity.com/paper/2405.20085/) (2024)
- [Prediction-Powered Communication with Distortion Guarantees](https://mlsanity.com/paper/2509.24373/) (2025)
- [Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications](https://mlsanity.com/paper/2503.06771/) (2025)
- [The Relativity of Causal Knowledge](https://mlsanity.com/paper/2503.11718/) (2025)
- [RIS-aided Latent Space Alignment for Semantic Channel Equalization](https://mlsanity.com/paper/2507.16450/) (2025)
- [Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2510.04674/) (2025)
- [Learning Consistent Causal Abstraction Networks](https://mlsanity.com/paper/2602.02623/) (2026)
- [Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications](https://mlsanity.com/paper/2411.01544/) (2024)
- [Goal-oriented Communications based on Recursive Early Exit Neural Networks](https://mlsanity.com/paper/2412.19587/) (2024)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [Pull-Based Query Scheduling for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2503.06725/) (2025)
- [An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading](https://mlsanity.com/paper/2602.13227/) (2026)
- [Federated Latent Space Alignment for Multi-user Semantic Communications](https://mlsanity.com/paper/2602.17271/) (2026)
- [SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations](https://mlsanity.com/paper/2605.09485/) (2026)
- [Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics](https://mlsanity.com/paper/2410.02303/) (2024)

## Abstract (arXiv)

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.