Abstract
Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a closed-loop architecture combining language supervision, episode memory, deterministic RF execution, perception feedback, and evidence-based reporting.} An independent auditor checks trajectory compliance. We derive acquisition and supervisory-request bounds, characterize full-history token complexity, and bound joint success by output validity and evidence capacity. \newhl{ActiveRF-AD tests instruction changes at fixed states and evaluates joint success, requiring compliance and RF correctness in the same episode.} \newhl{Training on different instructions at shared sensing states raises counterfactual-pair accuracy from 24.03\% to 96.22\% and Core joint success from 65.13\% to 72.12\%. Of the 6.99-point joint-success gain, 6.73 points reflect a decrease in the fraction of all episodes that are RF-correct but noncompliant.} \newhl{At Core budget three, paired-training hierarchical control achieves 66.60\% joint success versus 9.17\% for direct-action control.} \newhl{In a separate comparison at the same budget, RF-Agent improves joint success by 2.31 points and reduces RF acquisitions by 12.3\% relative to an initial-state planner, at increased inference cost.} Cross-backbone diagnostics cover three model families. These results show how instruction-responsive control improves task completion beyond what RF accuracy alone reveals.
Keywords
Publication details
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Cite this article
APA 7
Zhang, H., Xu, D., Zou, H., Bara, A., Mefgouda, B., Bariah, L., Yang, Y., Zhou, F., Wu, Q., & Debbah, M. (2026). RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing. https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing
MLA 9
Zhang, Hao, et al. "RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing." https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing.
Chicago (author–date)
Zhang, Hao, Dongfang Xu, Hang Zou, Anis Bara, Brahim Mefgouda, Lina Bariah, Yuzhi Yang, Fuhui Zhou, Qihui Wu, and Merouane Debbah. 2026. "RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing." https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing.
Harvard
Zhang, H., Xu, D., Zou, H., Bara, A., Mefgouda, B., Bariah, L., Yang, Y., Zhou, F., Wu, Q. and Debbah, M. (2026) 'RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing', Available at: https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing.
Vancouver
Zhang H, Xu D, Zou H, Bara A, Mefgouda B, Bariah L, et al. RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing. https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing
IEEE
H. Zhang, D. Xu, H. Zou, A. Bara, B. Mefgouda, L. Bariah, Y. Yang, F. Zhou, Q. Wu, and M. Debbah, "RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing," https://omanscience.com/en/articles/rf-agent-hierarchical-language-agent-control-for-instruction-conditioned-active-spectrum-sensing.