# Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC

arXiv: https://arxiv.org/abs/2604.02381 · HTML: https://mlsanity.com/paper/2604.02381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## 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, 19 of 19)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence](https://mlsanity.com/paper/2407.07061/) (2024)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Internet of Agents: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2505.07176/) (2025)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking](https://mlsanity.com/paper/2502.16866/) (2025)
- [Heterogeneous Embodied Multi-Agent Collaboration](https://mlsanity.com/paper/2307.13957/) (2023)
- [Feature Allocation for Semantic Communication with Space-Time Importance Awareness](https://mlsanity.com/paper/2401.14614/) (2024)
- [A Joint Communication and Computation Design for Distributed RISs Assisted Probabilistic Semantic Communication in IIoT](https://mlsanity.com/paper/2404.19750/) (2024)
- [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025)
- [Probabilistic Semantic Communication over Wireless Networks with Rate Splitting](https://mlsanity.com/paper/2403.00434/) (2024)
- [Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception](https://mlsanity.com/paper/2505.23275/) (2025)
- [Goal-Oriented Semantic Communication for Wireless Visual Question Answering](https://mlsanity.com/paper/2411.02452/) (2024)
- [Agentic AI Meets Edge Computing in Autonomous UAV Swarms](https://mlsanity.com/paper/2601.14437/) (2026)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI](https://mlsanity.com/paper/2509.08151/) (2025)
- [Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks](https://mlsanity.com/paper/2412.20937/) (2024)

## Abstract (arXiv)

The rapid development of agentic artificial intelligence (AI) is driving future wireless networks to evolve from passive data pipes into intelligent collaborative ecosystems under the emerging paradigm of integrated learning and communication (ILAC). However, realizing efficient agentic collaboration faces challenges not only in handling semantic redundancy but also in the lack of an integrated mechanism for communication, computation, and control. To address this, we propose a wireless agent network (WAN) framework that orchestrates a progressive knowledge aggregation mechanism. Specifically, we formulate the aggregation process as a joint energy minimization problem where the agents perform semantic compression to eliminate redundancy, optimize transmission power to deliver semantic payloads, and adjust physical trajectories to proactively enhance channel qualities. To solve this problem, we develop a hierarchical algorithm that integrates inner-level resource optimization with outer-level topology evolution. Theoretically, we reveal that incorporating a potential field into the topology evolution effectively overcomes the short-sightedness of greedy matching, providing a mathematically rigorous heuristic for long-term energy minimization. Simulation results demonstrate that the proposed framework achieves superior energy efficiency and scalability compared to conventional benchmarks, validating the efficacy of semantic-aware collaboration in dynamic environments.