arXiv AI July 13, 2026 By Cyril Shih-Huan Hsu, Anestis Dalgkitsis, Paola Grosso, Chrysa Papagianni

Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning

arXiv:2504. 18902v3 Announce Type: replace-cross Abstract: In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective management of Virtualized Network Functions (VNFs) is essential.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 13

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.

By Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Chongbo Sun, Xuefeng Tao, Wei Yang, Le Zhang, Wenwu Zhu, Peng Cui, Zhi Wang

llms safety

arXiv AI

Sep 23

From Inference Engine to Inference Control Plane: Connecting vLLM, llm-d, and the Evolution of Efficient Distributed LLM Serving

arXiv:2609.23130v1 Announce Type: new Abstract: Large language model (LLM) inference is evolving from an engine-local optimization problem into a distributed control problem involving reusable state,...

By Twinkll Sisodia

llms agents efficiency multimodal benchmarks

arXiv Machine Learning

Sep 14

MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling

MCRL2 is a reinforcement learning framework that enhances microservice scheduling in cloud data centers by integrating multi-resource cross-attention-based representation learning. It introduces MCRL, a representation learning component that captures structured interactions among nodes, resources, and microservices, and couples this with an actor‑critic architecture and a maximum entropy objective. Experiments on real production cluster traces show that MCRL2 outperforms existing baselines in load balancing, scheduling success rate, and average completion time across diverse workloads.

By Tiangang Li, Shi Ying, Xiangbo Tian, Chuan Shi, Ding Xiao

reinforcement-learning

arXiv Machine Learning

Jul 22

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.

By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas

reinforcement-learning

arXiv AI

Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 11

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.

By Ahasan Kabir, Jiaqi Xue, Mengxin Zheng, Qian Lou

llms agents reinforcement-learning benchmarks

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Related stories

Grounding Large Language Models as Generalizable Policies in Network Control

From Inference Engine to Inference Control Plane: Connecting vLLM, llm-d, and the Evolution of Efficient Distributed LLM Serving

MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration