SemanticXR is a device‑cloud system that enables real‑time, open‑vocabulary semantic mapping and querying for XR applications while respecting power, bandwidth, and memory limits. By treating semantically identifiable objects as first‑class units, the system coordinates communication, execution, and memory across device and server, achieving a 2.2× faster server‑side mapping latency and keeping upstream bandwidth below 2.5 Mbps. On the device, an object‑level sparse local map with incremental updates delivers sub‑100 ms query latency for up to 10,000 objects, supports tens of thousands of objects within a 500 MB footprint, and adds only about 2 % to idle power.
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.
arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...
arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.
arXiv:2511. 07885v5 Announce Type: replace-cross Abstract: Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.
MAPS is a Memory-Aware Predictive Scheduling framework designed for disaggregated large language model (LLM) serving. It uses device-assisted speculative output length prediction and uncertainty-aware calibration to establish safe output-length upper bounds, which inform a hierarchical global-local scheduling strategy that reduces queue buildup and head-of-line blocking. Experiments on real-world workloads and two LLMs demonstrate that MAPS lowers average end-to-end latency by 42.6% and tail latency by up to 84.8% compared to three state-of-the-art systems.
arXiv:2607. 14661v1 Announce Type: new Abstract: Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets.
arXiv:2502. 11007v5 Announce Type: replace Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn conversations.
arXiv:2511.03728v2 Announce Type: replace Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memo...
arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.
arXiv:2602. 04120v4 Announce Type: replace Abstract: Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient.
arXiv:2607. 00029v1 Announce Type: cross Abstract: Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in wilderness to leverage cloud resources or report critical information to remote centers.
HoliBench is a modular benchmarking and deployment toolkit that jointly measures accuracy, latency, and energy for foundation models across a wide range of devices, from single-board computers to GPU servers. It provides a platform abstraction layer that calibrates cross-device measurements and supports multiple model modalities, inference engines, and quantization levels. Using HoliBench, the authors evaluated 20 models on 7 device types, revealing tradeoffs such as limited latency gains from quantization on low‑precision hardware and diminishing accuracy returns relative to energy consumption, while also showing that single-model profiles can predict multi-model pipeline performance within a few percent.