This paper proposes a decentralized Multi-Agent Reinforcement Learning framework with a Graph Neural Network for connected and autonomous vehicles to reduce traffic shockwaves.

This paper proposes the first constant-time decoding algorithm for Augmented Gabidulin (AG) codes, a variation of Gabidulin codes used in efficient rank-based cryptosystems.

This paper proposes a scheme to coordinate 5G and TSN schedulers for supporting deterministic communications with bounded latencies in industrial applications.

This paper proposes Strip-based Predictor for Deformable Convolutional Networks (SPDCN) for steel surface defect segmentation, featuring Fuzzy-enhanced Multi-scale Context Module (FMCM) and Adaptive D…

The paper introduces CAFOSat, a large-scale, strongly annotated, and infrastructure-aware dataset designed to improve the accuracy of mapping Concentrated Animal Feeding Operations (CAFOs) from high-r…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

This paper critically re-evaluates the use of Graph Neural Networks (GNNs) for Bitcoin fraud detection, demonstrating that under strict, leakage-free temporal evaluation, simple feature-only models si…

The paper proposes a communication-centric 6G-LLM architecture for tactical autonomous defense vehicles, demonstrating significant improvements in coordination and communication efficiency over conven…

The paper introduces a Cognitive Taxonomy and a rigorous evaluation protocol to provide an objective, multi-faceted framework for measuring system capabilities and tracking progress toward Artificial…

The paper introduces CA-AC-MPC, a CUDA-accelerated variant of Actor-Critic Model Predictive Control, which significantly reduces the training and inference latency of AC-MPC while maintaining state-of…

A novel multi-agent reinforcement learning algorithm, TC-GQN, is proposed for high-level control and orchestration of mobile networks, enabling energy savings while maintaining QoS.

The paper introduces Influence-Guided Symbolic Regression (IGSR), a novel framework that uses granular influence scores to guide LLMs in efficiently searching for and discovering complex mathematical…

The paper introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) to achieve massive, structured compression of deep neural networks, demonstrating compression ratios up to 77,000x…

The paper proposes GC-MoE, a graph-conditioned Mixture of Experts framework, to improve traffic forecasting by assigning personalized, specialized forecasting experts to individual road segments.

The paper proposes AgentxGCore, an Agentic AI-Native layer that extends the 3GPP core network to enable self-organizing, self-adapting, and continuously optimized network management for 6G.

The paper proposes GCVE, a decentralized, open, and extensible socio-technical model to standardize and enrich the entire lifecycle of vulnerability information, moving beyond simple identifier alloca…

This paper develops a runtime recovery framework for broadcasting in dense Gaussian networks under static and dynamic faults, proving necessary and sufficient repair edges and providing efficient repa…