arXiv Machine Learning
Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)
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arXiv AI
6d ago
Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination
The paper introduces Embodied Semantic Communication (ESC), a new paradigm that redefines information transmission for autonomous agents by embedding multimodal perceptual states, hardware capabilities, and collaborative intents into unified, action‑oriented semantic representations. ESC enables heterogeneous agents to parse, align, and ground shared information directly into local motor control, addressing the limitations of traditional communication approaches that focus solely on bit delivery or single‑task optimization. The tutorial outlines ESC’s conceptual boundaries, system characteristics, and technical pathways, mapping relevant mathematical tools such as semantic information theory, world models, and multi‑agent decision theory, and concludes with a roadmap of open challenges like semantic reliability, dynamic interaction, and bandwidth‑adaptive transmission.
arXiv Machine Learning
Jul 9
Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices
arXiv:2607. 06651v1 Announce Type: new Abstract: Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and computational capacity.
arXiv Machine Learning
Sep 14
The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.
arXiv Machine Learning
Aug 27
Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role
The paper presents an autonomous agent that designs machine learning algorithms for wireless power control, eliminating manual specification of architecture, loss, and training details. Using an autoresearch protocol, the agent iteratively edits a training script, runs experiments, and evaluates changes against a single metric, ultimately achieving 99.5% of a reference solution with vastly reduced inference cost. The agent’s discovered output parameterization matches the exact max‑min‑optimal allocation at the minimum percentile for all trained weights, demonstrating a principled, scalable approach to a complex, NP‑hard problem.
Hugging Face Trending Papers
Sep 9
Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G
Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edg...
arXiv AI
Jul 20
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
arXiv:2607. 16133v1 Announce Type: cross Abstract: LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks.