arXiv:2606. 00417v1 Announce Type: cross Abstract: To meet the stringent requirements of emerging applications and the increasingly complex network management and operation, the Next Generation Mobile Networks (NextG), or 6G, will adopt an AI-native architecture on the Core Network (CN).
arXiv:2607. 06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime.
arXiv:2608. 10714v1 Announce Type: cross Abstract: The Organic 6G vision of a network of networks spanning an edge-cloud continuum complemented by non-terrestrial resources requires, to realize its promise, service provisioning that is simple to operate, scalable across independently administered domains, and agile under domain churn (i.
arXiv:2607. 16066v1 Announce Type: cross Abstract: Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs).
arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2610.33007v1 Announce Type: new Abstract: Natural-language service intents in integrated terrestrial and non-terrestrial (TN/NTN) 6G networks can be incomplete, ambiguous, or inconsistent, maki...
As the use of Artificial Intelligence (AI) and Large Language Models (LLMs) is becoming common in everyday applications, their ability to interpret natural language has increased significantly. An eme...
arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are po...
The paper introduces the Symbolic Guidance Taxonomy (SGT) to regulate the autonomy of large language model agents in distributed multi‑agent coordination. By providing symbolic guidance—ranging from open‑ended reasoning to fully prescriptive algorithmic steps—the authors find that intermediate levels of autonomy consistently outperform both unguided agents and fully prescriptive specifications. These results suggest that carefully controlling agent autonomy is a crucial design principle for effective LLM‑based coordination.
The paper introduces ANASSA, an agentic AI orchestration framework designed for spatial intelligence in geographic information systems. It addresses gaps in current systems by integrating structured spatial reasoning, multi‑agent workflow orchestration, execution feedback, authoritative validation, provenance, uncertainty handling, and human decision authority. The architecture is detailed with eleven components across four layers, a six‑step Geospatial AI Cognitive Loop, cross‑component contracts, and governance mechanisms to ensure traceability, reproducibility, and accountability.
The paper introduces ADN‑Agent, an architecture that uses a large language model to orchestrate multiple domain‑specific models (DSMs) for active distribution network (ADN) management. It features adaptive intent recognition, task decomposition, and a unified communication interface for heterogeneous DSMs, along with a pipeline for fine‑tuning small language models on language‑intensive subtasks. Experiments show ADN‑Agent outperforms existing LLM application paradigms in coordinating DSMs for complex ADN operations.