cs.NI · 2026-07-21 · No. 60

Networking and Internet Architecture, 2026-07-21.

3 new papers in cs.NI. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

    Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy, Ngoc-Thanh Nguyen, Rogardt Heldal, Patrizio Pelliccione

    cs.NI · cs.LG

    With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability. Modern LCSs continuously generate telemetry logs from distributed cloud services, producing high-dimensional multivariate time series that capture system operations. Detecting anomalies...

    arxiv.org/abs/2607.18127 · PDF

  2. 02

    Human Grounded Evaluation of Large Language Models for Optical Network Automation

    Kiarash Rezaei, Omran Ayoub, Paolo Monti, Carlos Natalino

    cs.NI · cs.AI

    Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert ratings to enable scalable and reproducible comparison of candidate LLMs, and to rank them using a quality efficiency score (QES). We demonstrate HuGLEN for...

    arxiv.org/abs/2607.18068 · PDF

  3. 03

    Mobile Network Control with a World Model

    Maxime Bouton, Ioanna Mitsioni, Simon Lindståhl, Jaeseong Jeong

    cs.NI · cs.AI

    The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from historical data and predicts the impact of its actions on future network states. Our controller leverages the model's uncertainty estimate to...

    arxiv.org/abs/2607.17747 · PDF

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