MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics

• URL: http://arxiv.org/abs/2606.11249v1
• Date: Mon, 08 Jun 2026 12:27:17 GMT
• Title: MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics
• Abstract summary: 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels.<n>We propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps.
• Score: 1.2604738912025477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels. In realistic collaborative sensing scenarios, spectral resources are quantized into finite physical resource blocks or orthogonal subcarriers, rendering simultaneous transmission by all agents infeasible. To address this, we propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps. We introduce Arbiter-Assisted Semantic Information Gating (A-SIG), a lightweight coordination mechanism that enforces hard access constraints by scheduling only the top-K agents based on locally computed semantic importance scores. By aggregating these prioritized observations into a compact latent state, a self-supervised global encoder enables a distributional policy to mitigate tail risks despite data sparsity. We evaluate MASK across diverse benchmarks, demonstrating that it matches the performance of communication-unconstrained baselines even when channel access is restricted to a small fraction of the swarm size. Furthermore, the framework exhibits inherent resilience to packet erasures, validating semantic scheduling as a critical enabler for resource-constrained 6G systems.

Related papers

• Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G [90.42669277637026]
  Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence.<n>This article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks.
  arXiv  Detail & Related papers  (2026-09-09T01:36:44Z)
• Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning [2.0965265618008315]
  passive, location-aware, static eavesdroppers without access to legitimate channel state information (CSI)<n>We propose secure adaptive authentication for multi-zone systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE)<n>Results under both colluding and non-colluding eavesdroppers show that the proposed method improves average SSE by up to 135% over single-connect transmission and 21% over the scheme without AN.
  arXiv  Detail & Related papers  (2026-09-08T21:46:12Z)
• An Efficient and Privacy-Preserving Architecture for Cross-Institutional Collaborative RAG [35.62196578084403]
  Retrieval-Augmented Generation empowers LLMs with external knowledge.<n>Strict privacy regulations create severe "data silos" that obstruct such collaboration.<n>We present FedRAG, a high- throughput, privacy-preserving federated RAG framework.
  arXiv  Detail & Related papers  (2026-05-25T11:18:19Z)
• SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking [11.675585308639839]
  Emergent communication offers a novel solution by enabling autonomous agents that support task-specific signaling protocols.<n>In this paper, we consider a multi-agent emergent communication framework, tailored for semantic-aware AgentNet systems.<n>Our proposed framework, called SANEmerg, is designed to facilitate the emergence of communication for collaborative task fulfillment.
  arXiv  Detail & Related papers  (2026-05-07T08:30:43Z)
• GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems [55.12339141172908]
  GoAgent is a communication topology generation method that explicitly treats collaborative groups as the atomic units of MAS construction.<n>Experiments on six benchmarks demonstrate the state-of-the-art performance of GoAgent with 93.84% average accuracy while reducing token consumption by about 17%.
  arXiv  Detail & Related papers  (2026-03-20T06:21:32Z)
• Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction [12.469641645103332]
  This paper proposes SoftGM, an octopus-inspired distributed control architecture for segmented soft robotic arms.<n>SoftGM formulates each arm section as a cooperative agent and represents the arm-environment interaction as a graph.<n>We evaluate SoftGM in a Cosserat-rod simulator (PyElastica) across three tasks that increase the complexity of the environment.
  arXiv  Detail & Related papers  (2026-03-10T19:56:07Z)
• Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping [61.459927600301654]
  Multi-condition control is bottlenecked by the conventional concatenate-and-attend'' strategy.<n>Our analysis reveals that much of this cross-modal interaction is spatially or semantically redundant.<n>We propose Position-aligned and Keyword-scoped Attention (PKA), a highly efficient framework designed to eliminate these redundancies.
  arXiv  Detail & Related papers  (2026-02-06T16:39:10Z)
• Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks [58.70163955407538]
  Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
  arXiv  Detail & Related papers  (2026-01-06T10:30:41Z)
• Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning [23.866517021196724]
  Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and communication links as edges.<n>We study this bandwidth-limited regime and show that naive dimensionality reduction consistently degrades coordination performance.<n>We introduce Band-constrained Variational Message (BVME), a lightweight module that treats messages as samples from Gaussians regularized via KL divergence to an uninformative prior.<n>BVME achieves comparable or superior performance while using 67--83% fewer message dimensions, with gains most pronounced on sparse graphs where message quality critically impacts coordination.
  arXiv  Detail & Related papers  (2025-12-11T23:56:43Z)
• Benchmarking LLMs' Swarm intelligence [51.648605206159125]
  Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) remains largely unexplored.<n>We introduce SwarmBench, a novel benchmark designed to systematically evaluate tasks of LLMs acting as decentralized agents.<n>We propose metrics for coordination effectiveness and analyze emergent group dynamics.
  arXiv  Detail & Related papers  (2025-05-07T12:32:01Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.