The paper introduces EMMI, a framework that enables communication‑efficient inference of multimodal large language models (MLLMs) on edge devices. EMMI encodes each sensor modality separately, fuses the representations, and compresses them into a compact latent vector that is transmitted to a server for high‑capacity reasoning. Experiments on a multimodal benchmark show that EMMI can cut the communication payload by 32× while keeping accuracy comparable, achieving up to a 3.4× reduction in end‑to‑end inference latency under bandwidth‑constrained conditions.
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: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:2609.10714v1 Announce Type: cross Abstract: The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and ta...
The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.
arXiv:2512. 16349v2 Announce Type: replace-cross Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintaining inference accuracy.
arXiv:2607. 09063v1 Announce Type: new Abstract: Edge devices are increasingly utilized for deploying deep learning applications on embedded systems.
arXiv:2605. 28642v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated significant potential for speech-to-text translation (S2TT).
arXiv:2607. 12111v1 Announce Type: cross Abstract: Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges.
The paper introduces ModalShare, a bandwidth allocation method for multimodal split learning that assigns each modality a keep‑ratio based on its Shapley contribution score. Unlike existing compression schemes that split the uplink budget proportionally to activation size, ModalShare explicitly optimizes the split across modalities, requiring no extra uplink traffic or client computation. Experiments on CREMA‑D and MVSA datasets show that ModalShare improves accuracy by 12.4–15.4 percentage points over equal keep‑ratios under a 5× compression budget, outperforming three compressors across multiple datasets and budgets.
arXiv:2607. 06982v1 Announce Type: cross Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks.
Diffusion language models (DLMs) provide a non‑autoregressive approach for mobile edge agentic AI, refining tokens through iterative denoising instead of left‑to‑right decoding. They can update multiple uncertain tokens in parallel and use bidirectional context, allowing flexible quality‑latency trade‑offs and early exits that reduce response delay and communication overhead. The survey reviews DLM foundations, resource‑efficient architectures, training and inference acceleration, compression, deployment strategies, and discusses open issues such as long‑context management, split inference, and trustworthy execution.
arXiv:2607. 17913v1 Announce Type: cross Abstract: Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead.