Public articles linked to the same research event.
arXiv The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.
The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.
The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.
The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.