# SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G

arXiv: https://arxiv.org/abs/2512.22579 · HTML: https://mlsanity.com/paper/2512.22579/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 31 of its 63 references that are in the map.
- First posted on arXiv 27 Dec 2025.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 30 of 31)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [Small Language Models are the Future of Agentic AI](https://mlsanity.com/paper/2506.02153/) (2025)
- [Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration](https://mlsanity.com/paper/2406.01014/) (2024)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://mlsanity.com/paper/2402.18272/) (2024)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Rethinking Bias-Variance Trade-off for Generalization of Neural Networks](https://mlsanity.com/paper/2002.11328/) (2020)
- [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023)
- [Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?](https://mlsanity.com/paper/2502.00674/) (2025)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance](https://mlsanity.com/paper/2305.20057/) (2023)
- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems](https://mlsanity.com/paper/2206.07082/) (2022)
- [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2505.01074/) (2025)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet](https://mlsanity.com/paper/2001.10199/) (2020)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [6G Network AI Architecture for Everyone-Centric Customized Services](https://mlsanity.com/paper/2205.09944/) (2022)
- [The Bridge Toward 6G: 5G-Advanced Evolution in 3GPP Release 19](https://mlsanity.com/paper/2312.15174/) (2023)
- [Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges](https://mlsanity.com/paper/2409.05092/) (2024)
- [SANSee: A Physical-layer Semantic-aware Networking Framework for Distributed Wireless Sensing](https://mlsanity.com/paper/2410.19795/) (2024)
- [Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding](https://mlsanity.com/paper/2311.09879/) (2023)
- [SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination](https://mlsanity.com/paper/2505.18946/) (2025)
- [Enhancing Immersion and Presence in the Metaverse with Over-the-Air Brain-Computer Interface](https://mlsanity.com/paper/2303.10577/) (2023)

## Built on by (most cited first, 5 of 5)

- [Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences](https://mlsanity.com/paper/2605.02584/) (2026)
- [SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking](https://mlsanity.com/paper/2605.05861/) (2026)
- [Generalization Bounds of Emergent Communications for Agentic AI Networking](https://mlsanity.com/paper/2605.08613/) (2026)
- [Implicit Semantic-Aware Communication Based on Hypergraph Reasoning](https://mlsanity.com/paper/2606.20162/) (2026)
- [Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking](https://mlsanity.com/paper/2608.00104/) (2026)

## Abstract (arXiv)

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decision-making, dynamic environmental adaptation, and complex missions. It has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks that can infer the semantic goal of the user and automatically assign agents associated with different layers of the network to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms are proposed. We derive theoretical bounds and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. We develop an open-source RAN and core network-based hardware prototype that implements agents to interact with three different layers of the network. Experimental results show that the proposed framework achieved performance gains of up to 14.61% while requiring only 44.37% of FLOPs required by state-of-the-art algorithms.