Multimodal Sensing Fusion-Based LLM Agent Methods, System, and Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 25
Special Issue Editor
Interests: multimodal large language models; lightweight large language models; robust and secure large language models; computer vision; AIGC; LLM
Special Issue Information
Dear Colleagues,
The integration of multimodal sensor data with large language model (LLM) agents represents an important frontier in artificial intelligence as it combines heterogeneous streams of information to advance machine perception, reasoning, and interaction. LLM agents, which are trained on extensive and diverse datasets, have demonstrated strong capabilities in language understanding, contextual inference, and complex decision-making. When augmented with multimodal sensing that incorporates visual, auditory, speech, tactile, LiDAR, radar, physiological, and other forms of data, these agents can achieve more comprehensive situational awareness and higher adaptability in dynamic and uncertain environments. Such integration not only improves real-time decision-making and enhances human–machine interaction but also creates new opportunities for innovation across a wide range of domains including healthcare, robotics, autonomous systems, smart manufacturing, education, environmental monitoring, and security. At the same time, it raises fundamental research challenges related to trustworthiness, interpretability, and ethical considerations in the deployment of intelligent agents. This Special Issue aims to bring together original contributions that explore methodologies, theories, and applications at the intersection of multimodal sensing and LLM-based intelligence, with particular emphasis on advances in multimodal fusion strategies, contextual reasoning, adaptive dialogue systems, collaborative and immersive applications, as well as cross-disciplinary approaches that contribute to the development of next-generation multimodal artificial intelligence.
Prof. Dr. Chaoning Zhang
Guest Editor
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Keywords
- multimodal sensor fusion
- large language model (LLM) agents
- multimodal perception and reasoning
- contextual understanding
- adaptive dialogue systems
- human–machine interaction
- trustworthy and explainable AI
- robotics and autonomous systems
- healthcare and smart manufacturing
- cognitive intelligence and situational awareness
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