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arXiv AI By Zhongqin Wang, Xiaoqi Zhang, Nan Yang, Kai Wu, J. Andrew Zhang, Y. Jay Guo

Agentic Semantic Sensing for Resource-Adaptive AI-RAN

Read the original on arXiv AI →

The paper introduces Agentic Semantic Sensing (Agentic SemS), a closed‑loop framework for AI‑enabled radio access networks that dynamically adjusts sensing configurations within a communication‑feasible profile set. A profile‑conditioned causal Transformer updates semantic beliefs from streaming data, while a semantic utility network guides the selection of the next sensing profile and determines when to exit early, balancing task benefit against sensing cost. Experiments on Widar3.0 demonstrate that Agentic SemS reduces cumulative sensing cost by 25.33% compared to a full‑sequence baseline while maintaining 85.79% Macro‑F1, and that semantic early exit yields an additional 12.35% cost savings with minimal performance loss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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