arXiv:2511.14478v4 Announce Type: replace-cross Abstract: Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive review that establishes a precise definition and taxonomy for "agentic AI," with the aim of distinguishing it from previous AI paradigms. The concepts are gradually introduced, starting with a highlight of its diverse applications across the broader field of engineering. The paper then presents four detailed, state-of-the-art use case applications specifically within electrical engineering. These case studies demonstrate practical impact, ranging from an advanced agentic framework for streamlining complex power system studies and benchmarking to a novel system developed for survival analysis of dynamic pricing strategies in battery swapping stations. Finally, to ensure robust deployment, the paper provides detailed failure mode investigations. From these findings, we derive actionable recommendations for the design and implementation of safe, reliable, and accountable agentic AI systems, offering a critical resource for researchers and practitioners.

arXiv:2602.13606v1 Announce Type: cross Abstract: Millimeter wave (mmWave) communication, utilizing beamforming techniques to address the inherent path loss limitation, is considered as one of the key technologies to support ever increasing high throughput and low latency demands of connected vehicles. However, adopting standard defined beamforming approach in highly dynamic vehicular environments often incurs high beam training overheads and reduction in the available airtime for communications, which is mainly due to exchanging pilot signals and exhaustive beam measurements. To this end, we present a multi-modal sensing and fusion learning framework as a potential alternative solution to reduce such overheads. In this framework, we first extract the representative features from the sensing modalities by modality specific encoders, then, utilize multi-head cross-modal attention to learn dependencies and correlations between different modalities, and subsequently fuse the multimodal features to obtain predicted top-k beams so that the best line-of-sight links can be proactively established. To show the generalizability of the proposed framework, we perform a comprehensive experiment in four different vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scenarios from real world multimodal and 60 GHz mmWave wireless sensing data. The experiment reveals that the proposed framework (i) achieves up to 96.72% accuracy on predicting top-15 beams correctly, (ii) incurs roughly 0.77 dB average power loss, and (iii) improves the overall latency and beam searching space overheads by 86.81% and 76.56% respectively for top-15 beams compared to standard defined approach.

arXiv:2512.15829v2 Announce Type: replace-cross Abstract: Working memory enables the brain to integrate transient information for rapid decision-making. Artificial networks typically replicate this via recurrent or parallel architectures, yet incur high energy costs and noise sensitivity. Here we report IPNet, a hardware-software co-designed neuromorphic architecture realizing human-like working memory via neuronal intrinsic plasticity. Exploiting Joule-heating dynamics of Magnetic Tunnel Junctions (MTJs), IPNet physically emulates biological memory volatility. The memory behavior of the proposed architecture shows similar trends in n-back, free recall and memory interference tasks to that of reported human subjects. Implemented exclusively with MTJ neurons, the architecture with human-like working memory achieves 99.65% accuracy on 11-class DVS gesture datasets and maintains 99.48% on a novel 22-class time-reversed benchmark, outperforming RNN, LSTM, and 2+1D CNN baselines sharing identical backbones. For autonomous driving (DDD-20), IPNet reduces steering prediction error by 14.4% compared to ResNet-LSTM. Architecturally, we identify a 'Memory-at-the-Frontier' effect where performance is maximized at the sensing interface, validating a bio-plausible near-sensor processing paradigm. Crucially, all results rely on raw parameters from fabricated devices without optimization. Hardware-in-the-loop validation confirms the system's physical realizability. Separately, energy analysis reveals a reduction in memory power of 2,874x compared to LSTMs and 90,920x versus parallel 3D-CNNs. This capacitor-free design enables a compact ~1.5um2 footprint (28 nm CMOS): a >20-fold reduction over standard LIF neurons. Ultimately, we demonstrate that instantiating human-like working memory via intrinsic neuronal plasticity endows neural networks with the dual biological advantages of superior dynamic vision processing and minimal metabolic cost.