arXiv AI
Jul 24
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
arXiv:2607. 20937v1 Announce Type: new Abstract: We are moving from an information age to the age of intelligence.
Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications
arXiv:2607. 19676v1 Announce Type: new Abstract: Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the network edge.
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arXiv AI
Jul 24
CRAWO: Custom Resources for Adaptive Workload Orchestration
arXiv:2607. 20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data centers to the network edge, thereby reducing latency and bandwidth consumption.
arXiv AI
Jun 11
Resource-Aware LLM Reasoning for Mobile Edge General Intelligence
arXiv:2509. 23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.
arXiv AI
Jun 9
Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
arXiv:2606. 09122v1 Announce Type: cross Abstract: Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures.
arXiv AI
Aug 7
When Agentic AI Meets Integrated Sensing and Communication
arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.
arXiv AI
Jul 7
Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents
arXiv:2607. 04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization.
arXiv AI
22h ago
Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum
arXiv:2605. 09623v2 Announce Type: replace-cross Abstract: In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly.
arXiv AI
Jul 17
EdgeFaaS: A Function-based Framework for Edge Computing
arXiv:2607. 14489v1 Announce Type: cross Abstract: Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world.
arXiv AI
Jun 2
A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks
arXiv:2606. 01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems.
Hugging Face Trending Papers
Jul 16
EdgeFaaS: A Function-based Framework for Edge Computing
Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution.
arXiv Machine Learning
Jun 4
CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles
arXiv:2606. 04072v1 Announce Type: cross Abstract: Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer.
arXiv AI
Aug 12
Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.