mlsanityData to 7 Oct 2026

Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges

arXiv 2510.20637 ↗First posted 23 Oct 2025cs.LGcited by 0

On the map: Multimodal LLMs / vision-language models → Driving VLMs and open-vocabulary detection

Abstract (arXiv)

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Driving VLMs and open-vocabulary detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 9 of its 13 references that are in the map. First posted on arXiv 23 Oct 2025.

Builds on

9 of 13 references.

References · 9

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2024TinyLLaVA: A Framework of Small-scale Large Multimodal Modelscited by 164
  4. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  5. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  6. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  7. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  8. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  9. 2024Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Surveycited by 6

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  1. 2025Large Language Models for Wireless Communications: From Adaptation to Autonomy
  2. 2023A Survey on Multimodal Large Language Models for Autonomous Driving
  3. 2025Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks
  4. 2024XLM for Autonomous Driving Systems: A Comprehensive Review
  5. 2024Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
  6. 2026Large Multimodal Model-Based Environment-Aware Mobility Management
  7. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
  8. 2023Large AI Model Empowered Multimodal Semantic Communications