# Agentic Link Construction for Environment and Intent Aware 6G Communication

arXiv: https://arxiv.org/abs/2511.05094 · HTML: https://mlsanity.com/paper/2511.05094/

## 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 9 of its 16 references that are in the map.
- First posted on arXiv 7 Nov 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, 9 of 9)

- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [LLM4CP: Adapting Large Language Models for Channel Prediction](https://mlsanity.com/paper/2406.14440/) (2024)
- [Large Wireless Model (LWM): A Foundation Model for Wireless Channels](https://mlsanity.com/paper/2411.08872/) (2024)
- [WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication](https://mlsanity.com/paper/2502.06877/) (2025)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [6G WavesFM: A Foundation Model for Sensing, Communication, and Localization](https://mlsanity.com/paper/2504.14100/) (2025)
- [Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation](https://mlsanity.com/paper/2505.12729/) (2025)

## Abstract (arXiv)

The emergence of sixth-generation networks heralds an intelligent communication ecosystem driven by the rapid proliferation of intelligent services and increasingly complex communication scenarios. However, current physical-layer designs-typically following modular and isolated optimization paradigms-fail to achieve global end-to-end optimality due to neglected inter-module dependencies. Although large language models (LLMs) have recently been applied to communication tasks such as beam prediction and resource allocation, existing studies remain limited to single-task or single-modality scenarios and lack the ability to jointly reason over communication states and user intents for personalized strategy adaptation. To address these limitations, this paper proposes a novel multimodal communication decision-making model for link construction leveraging reinforcement learning on pretrained LLMs. The proposed model semantically aligns channel state information (CSI) and textual user instructions, enabling comprehensive understanding of both physical-layer conditions and communication intents. It then generates physically realizable, user-customized link construction that dynamically adapts to changing environments and preference tendencies. A two-stage reinforcement learning framework is employed: the first stage expands the experience pool via heuristic exploration and behavior cloning to obtain a near-optimal initialization, while the second stage fine-tunes the model through multi-objective reinforcement learning considering BER, throughput, and power consumption. Experimental results demonstrate that the proposed model significantly outperforms conventional planning-based algorithms under challenging channel conditions, achieving robust, efficient, and personalized end-to-end communication strategies.