# Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception

arXiv: https://arxiv.org/abs/2505.23275 · HTML: https://mlsanity.com/paper/2505.23275/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 29 May 2025.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Benchmarking Reasoning Robustness in Large Language Models](https://mlsanity.com/paper/2503.04550/) (2025)
- [Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG](https://mlsanity.com/paper/2503.01222/) (2025)
- [Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation](https://mlsanity.com/paper/2308.04942/) (2023)
- [The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence](https://mlsanity.com/paper/2501.06471/) (2025)
- [Compression Ratio Learning and Semantic Communications for Video Imaging](https://mlsanity.com/paper/2310.06246/) (2023)
- [Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion](https://mlsanity.com/paper/2503.17777/) (2025)

## Built on by (most cited first, 4 of 4)

- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks](https://mlsanity.com/paper/2601.19607/) (2026)
- [Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC](https://mlsanity.com/paper/2604.02381/) (2026)
- [Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management](https://mlsanity.com/paper/2605.03215/) (2026)

## Abstract (arXiv)

The rapid development of multimodal AI and Large Language Models (LLMs) has greatly enhanced real-time interaction, decision-making, and collaborative tasks. However, in wireless multi-agent scenarios, limited bandwidth poses significant challenges to exchanging semantically rich multimodal information efficiently. Traditional semantic communication methods, though effective, struggle with redundancy and loss of crucial details. To overcome these challenges, we propose a Retrieval-Augmented Multimodal Semantic Communication (RAMSemCom) framework. RAMSemCom incorporates iterative, retrieval-driven semantic refinement tailored for distributed multi-agent environments, enabling efficient exchange of critical multimodal elements through local caching and selective transmission. Our approach dynamically optimizes retrieval using deep reinforcement learning (DRL) to balance semantic fidelity with bandwidth constraints. A comprehensive case study on multi-agent autonomous driving demonstrates that our DRL-based retrieval strategy significantly improves task completion efficiency and reduces communication overhead compared to baseline methods.