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.
By Bowen Lu, Mugen Peng, Yaohua Sun, Hongyu Wang, Kerui Guo, Wenjia Xu
arXiv:2610.35282v1 Announce Type: cross
Abstract: The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks...
By Zheng Lin, Zhanwei Wang, Yuxin Zhang, Yafei Wang, Ons Aouedi, Zihan Fang, Jinfeng Xu, Jing Yang, Vu Nguyen Ha, Yuanwei Liu, Mohamed-Slim Alouini, Symeon Chatzinotas
The paper introduces ALMOND, a cognitive framework that bridges the gap between human intent and low‑level control logic for 6G low Earth orbit satellite networks. It employs a bidirectional semantic engine to convert natural‑language requests into multi‑objective reward functions and extracts operational constraints from failed reinforcement‑learning trajectories, while a large‑language‑model‑guided graph neuroevolution designs efficient policy architectures. In a 350‑satellite digital twin, ALMOND improves polar handover success from 55% to over 95%, speeds convergence threefold, and cuts reward‑engineering time from 48 to 4.5 hours, all within a compute‑frugal on‑board policy of under 50,000 parameters.
By Zhengqian Xu, Yasheng Zhang, Chenhua Sun, Shan Lu, Chengcheng Li
arXiv:2608. 03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale.
By Ziwu Liu, In\^es Pinto Gouveia, Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker
The paper introduces TELGEN, a traffic engineering algorithm that uses graph neural networks to predict an optimal TE algorithm rather than a direct solution. TELGEN generalizes across diverse network topologies and traffic patterns, achieving less than a 3% optimality gap on networks up to 5,000 nodes and 3.6 million links, while reducing solving time by up to 84% and training time by up to 79.6% compared to existing methods.
By Fangtong Zhou, Xiaorui Liu, Ruozhou Yu, Guoliang Xue
arXiv:2606. 12479v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as an effective paradigm for leveraging the complementary strengths of multiple LLMs through dynamic model and reasoning-strategy selection.
By Qihang Yu, Hanwen Tong, Zhengqi Zhang, Bo Zheng, Feng Wei, Shengyu Zhang, Zemin Liu, Fei Wu
The paper proposes an online learning framework for configuring dynamic constellation topologies in satellite networks, addressing the challenges posed by continuous orbital movement and node maneuvering. It does not rely on predefined orbital plane structures, making it robust to changes caused by satellite maneuvers. Experiments show that the method performs comparably to state‑of‑the‑art offline techniques and can be adapted to constrained online learning, balancing per‑iteration computational cost against convergence speed.
By Jo\~ao Norberto, Ricardo Ferreira, Cl\'audia Soares
arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
By Ritik Raj, Souvik Kundu, Sarbartha Banerjee, Dheemanth Joshi, Ishita Vohra, Tushar Krishna
arXiv:2610.19705v1 Announce Type: new
Abstract: LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built arou...
By Xiaohan Wang, Haozhen Zhang, Qingyuan Liu, Tao Feng, Wenya Wang
arXiv:2601. 06188v3 Announce Type: replace Abstract: As Earth-observing satellite constellations grow in size and capability, distributed onboard control offers a pathway to novel responses and time-sensitive measurements.
By Itai Zilberstein, Steve Chien
Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning...
The paper introduces a stability‑aware autoscaling framework for edge serverless workloads that combines an Attention‑Enhanced Double‑Stacked LSTM with Proximal Policy Optimization to address temporal blindness in deep reinforcement learning. By weighting recent historical states non‑uniformly, the method suppresses high‑frequency jitter while preserving demand trends, outperforming single‑layer LSTM, static HPA, and KEDA baselines in latency reduction and stability. Experiments on two Kubernetes clusters with real Azure Functions traces show a ~67% reduction in P90 latency and improved adherence to a 50 ms hard SLO.
By Faraz Shaikh, Gianluca Reali, Mauro Femminella