Large Language Models (LLMs) have revolutionized natural language processing, but they lack autonomous interaction capabilities. LLM agents address this by integrating three key modules, such as memory, planning, and tool use modules, around the LLM core. This survey systematically analyzes LLM agents through their architectural components, control flow paradigms, and application domains spanning data analysis, software engineering, and social simulation. We identify critical challenges in reliability, evaluation, and scalability, and outline future research directions. This work provides a structured overview of the rapidly evolving landscape of LLM agents.
In this research, we are the first to examine the potential and practicality of achieving a quantum computing advantage within communication relay networks. Barrage Relay networks (BRNs) offer a low-latency and resilient network structure capable of preventing collisions through self-directed cooperation, thus enabling a robust low-latency broadcast system. We extend BRN by designing a multi-hop quantum teleportationbased relay network. This work discovers a theoretical framework for a multi-relay strategy for quantum teleportation tailored for tactical ad-hoc networking in the post-quantum era, utilizing quantum devices along with preliminary experimental analyses to support our proposed protocol using Qiskit.
In this paper, we propose a robust optimizationbased mobile base station placement method for efficient store-carry-forward data collection using unmanned aerial vehicles (UAVs) in environments with limited or disrupted communication infrastructure. We focus on scenarios in which communication demand is temporally fluctuating and uncertain, with the aim to determine a placement strategy that maintains stable performance under varying future conditions. We formulate the base station placement problem as a robust optimization problem that explicitly accounts for demand uncertainty. We obtain the optimal solution using the column-and-constraint generation algorithm. To evaluate the effectiveness of the proposed approach, we conducted simulation experiments with predefined candidate sites and demand points, and compared the results with those of benchmark methods. The proposed method significantly reduces both the total deployment cost and number of installed base stations while maintaining a high data collection rate and balanced demand coverage, which demonstrates its robustness and efficiency, even under probabilistic demand fluctuations.
Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments. Managed Kubernetes is the powerhouse orchestrating cloud native applications with elastic scaling. However, traditional Kubernetes autoscalers are reactive, meaning the scaling controllers adjust resources only after they detect demand within the cluster and do not incorporate any predictive measures. This can lead to either over-provisioning and increased costs or under-provisioning and performance degradation. We propose NimbusGuard, an open-source, Kubernetes-based autoscaling system that leverages a deep reinforcement learning agent to provide proactive autoscaling. The agents perception is augmented by a Long Short-Term Memory model that forecasts future workload patterns. The evaluations were conducted by comparing NimbusGuard against the built-in scaling controllers, such as Horizontal Pod Autoscaler, and the event-driven autoscaler KEDA. The experimental results demonstrate how NimbusGuard's proactive framework translates into superior performance and cost efficiency compared to existing reactive methods.
In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventually choose not to share it at all. Based on the mean-field approximation method, we derive the dynamical equations of the model and investigate its convergence and stability conditions. Under these conditions, we further propose an approximation algorithm for the edge-deletion problem, aiming to minimize the influence of information diffusion by identifying approximate solutions.
