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.
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:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...
STR-Agent is an LLM-driven framework designed for QoS-aware routing in Low Earth Orbit satellite networks. It integrates intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation to translate natural-language service requests into adaptive routing decisions. In simulations on a Walker-Delta constellation, STR-Agent reduces end-to-end delay by up to 60% compared with DQ-Dijkstra and improves intent-understanding accuracy from 45.4% to 92.45% after fine-tuning, with the Reflection Module providing additional delay reductions.
The paper introduces FedTAR, a task-aware federated fine‑tuning approach for Mixture‑of‑Experts (MoE) large language models. FedTAR links local client updates to task preferences using routing outputs and Singular Value Decomposition to extract low‑dimensional task coordinates and update directions. It then aggregates updates within and across task clusters, reconstructing the final update to preserve expert specialization and reduce interference, achieving state‑of‑the‑art performance on four benchmark tasks under non‑IID settings.
arXiv:2606. 03557v1 Announce Type: new Abstract: As generative AI capabilities expand, AI-driven virtual worlds face a growing architectural challenge.
arXiv:2512. 20627v2 Announce Type: replace-cross Abstract: Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT) environments by translating high-level user intents into executable network strategies.
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).
OrchSLM is a routing framework that unifies non‑interactive orchestration methods for small language models (SLMs). It allows heterogeneous SLMs to independently generate candidate solutions while a router manages their cached outputs without further model interaction. By systematically probing OrchSLM, the study shows how orchestration behavior depends on task structure, model‑pool composition, and multi‑agent consensus.
arXiv:2606. 15079v1 Announce Type: cross Abstract: Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy.
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
The paper introduces agentic-eCAL, an extension of the Energy Cost of AI Lifecycle metric to evaluate multi‑agent AI workflows across the edge‑cloud continuum. By combining a two‑rate energy model with OSI‑layer transport analysis, the authors quantify that inter‑agent text transfer accounts for only 0.25% of total workflow energy, highlighting that the main energy cost lies in additional inference and context processing triggered by communication. The study uses extensive GPU benchmarks on NVIDIA A100/H100 with 16 open‑weight models and 8 orchestration topologies to validate the metric and explore placement implications.
arXiv:2606. 22902v3 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all.