This paper proposes a self-evolving agentic artificial intelligence (AI) framework for low-altitude wireless networks (LAWNs), introducing integrated sensing and communication (ISAC) into a unified self-evolution paradigm that transforms static foundation models with passive perception into fully autonomous, self-evolving agentic systems. The framework integrates ISAC-enabled perception, reasoning, evolution, task-specific decision-making, and knowledge memory access into a unified self-evolving agentic architecture, enabling iterative improvement of cognition, task understanding, and decision evaluation capabilities. We further introduce a self-evolving deliberative agentic AI mechanism, in which agents generate multiple candidate actions and perform consistency-based evaluation before execution. This reason-before-act approach shifts decision-making from reactive responses to rational deliberation, enabling proactive decision-making and long-horizon optimization. A case study on the integrating sensing, communication and control task in LAWN demonstrates that the proposed framework significantly enhances data rate and sensing error performances.
Low-altitude wireless networks (LAWNs) are expected to support mission-critical services in future sixth-generation systems, with tightly integrated communication, sensing, computation, and control. Beyond task-specific intelligence, emerging applications increasingly require autonomous behavior, explicit handling of m...
Yao Yu, Wei-Jie Yuan, Yu-Lin Liu et al.· IEEE wireless communications· 0 citations
As low-altitude applications expand across emergency response, intelligent transportation, and autonomous operations, they demand communication networks that can deliver flexible, resilient, and rapidly deployable connectivity. Heterogeneous UAV networks are a promising solution, as they can dynamically provide sensing...
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The PhysAI-Bench is introduced, a benchmark for evaluating the agentic decision-making required for reliable autonomy in Physical AI, which contains 10,178 standardized decision instances automatically extracted from conversational traces of autonomous UAV missions.
M. Ferrag, Mérouane Debbah, Abderrahmane Lakas et al.· 0 citations
Future low-altitude wireless networks (LAWNs) are evolving from simple connectivity layers into intelligent fabrics populated by goal-driven aerial agents. In this emerging landscape, unmanned aerial vehicles (UAVs) must autonomously navigate complex tradeoffs between mission-critical objectives, such as timely deliver...
Bin Liu, Wei Ni, Rafael F. Schaefer et al.· IEEE Communications Magazine· 0 citations
Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations, dynamic environments, and intermittent connectivity, while many existing agentic systems remain limited by hallucination risks, data dependence, and weak generalization...
Yu-Qi Ping, Tian-Hao Liang, Nan-Chi Su et al.· 0 citations
A comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI is proposed, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance and aims to maximize the benefits of Agentic AI while minimizing potential risks.
Nitin S. Shrirao, Dnyaneshwar S. Jadhav, Sarita B. Patil· Recent Trends in Mathematics· 0 citations
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