mlsanityData to 7 Oct 2026

Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks

arXiv 2511.21707 ↗First posted 17 Nov 2025cs.NI cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Large models (LMs), such as ChatGPT, have made a significant impact across diverse domains and hold great potential to facilitate the evolution of network intelligence. Wireless-native multi-modal large models (WMLMs) can sense and understand the physical world through multi-modal data, serving as a key enabler that integrates communication, sensing, and intelligence, and thus they can boost various smart services to billions of users. However, research on WMLMs remains in its infancy, and the construction of domain-specific multi-modal large models for wireless networks is still underexplored. In this paper, we outlines the key characteristics of WMLMs and summarizes existing methods, on the basis of which a wireless-native multimodal training paradigm is proposed. Specifically, we constructed a GPT-style WMLM model and trained it on a real-world large-scale dataset, leveraging wireless signals as an anchor modality for contrastive learning. Our approach demonstrates outstanding performance compared with existing small-scale models and large multi-modal models, validating the feasibility of using wireless signals as a universal modality and highlighting WMLM's potential to emerge as a new paradigm for future wireless networks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 12 of its 15 references that are in the map. First posted on arXiv 17 Nov 2025.

Builds on

12 of 15 references.

References · 12

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2025Qwen2.5-VL Technical Reportcited by 5,418
  3. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,162
  4. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  5. 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
  6. 2022DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Datasetcited by 53
  7. 2024LLM4CP: Adapting Large Language Models for Channel Predictioncited by 42
  8. 2024Beam Prediction based on Large Language Modelscited by 20
  9. 2023Generative AI for Physical Layer Communications: A Surveycited by 17
  10. 2024Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensingcited by 15
  11. 2025LLM4WM: Adapting LLM for Wireless Multi-Taskingcited by 15
  12. 2023Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machinescited by 14

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  1. 2024Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
  2. 2026A Wireless World Model for AI-Native 6G Networks
  3. 2025Large Language Models for Wireless Communications: From Adaptation to Autonomy
  4. 2026Large Multimodal Model-Based Environment-Aware Mobility Management
  5. 2024ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception
  6. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
  7. 2023Large AI Model Empowered Multimodal Semantic Communications
  8. 2025Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges