Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks

• URL: http://arxiv.org/abs/2511.17505v1
• Date: Thu, 02 Oct 2025 14:34:59 GMT
• Title: Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks
• Abstract summary: We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold.<n>Our model then learns the causal chain that turns normal behavior into a fault.<n>In Monte Carlo tests the approach pinpoints the correct trigger sequence with high precision and scales to millions of data points without loss of speed.
• Score: 18.99444800453706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To keep modern Radio Access Networks (RAN) running smoothly, operators need to spot the real-world triggers behind Service-Level Agreement (SLA) breaches well before customers feel them. We introduce an AI/ML pipeline that does two things most tools miss: (1) finds the likely root-cause indicators and (2) reveals the exact order in which those events unfold. We start by labeling network data: records linked to past SLA breaches are marked `abnormal', and everything else `normal'. Our model then learns the causal chain that turns normal behavior into a fault. In Monte Carlo tests the approach pinpoints the correct trigger sequence with high precision and scales to millions of data points without loss of speed. These results show that high-resolution, causally ordered insights can move fault management from reactive troubleshooting to proactive prevention.

Related papers

• FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models [12.030216781017558]
  Vision-language--action (VLA) policies can fail unpredictably during long-horizon execution.<n>We propose a data-efficient framework that first leverages unlabeled VLA action chunks to construct weak supervision signals.<n>We then use active learning to select only the most uncertain trajectories for timestamp-level annotation.
  arXiv  Detail & Related papers  (2026-09-03T00:04:06Z)
• Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems [47.66236392009794]
  Large Language Model(LLM)-based agents have demonstrated exceptional performance across a wide range of complex interactive tasks.<n>We propose textitTrajectory Graph Copilot, a novel framework that acts as a copilot'' for LLM agents by diagnosing potential action errors before they are executed.
  arXiv  Detail & Related papers  (2026-07-29T20:14:43Z)
• PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis [13.502332249072696]
  This paper proposes PropLLM, which is the first to integrate the hop-by-hop scene reconstruction paradigm with the generative reasoning capabilities of LLMs.<n>On a real-world Wi-Fi multimodal fault dataset, PropLLM improves fault type diagnosis accuracy by 3.9% and root cause localization accuracy by 4.7%.
  arXiv  Detail & Related papers  (2026-05-30T07:18:42Z)
• AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
  We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
  arXiv  Detail & Related papers  (2026-03-15T16:13:58Z)
• GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL [64.8155693023222]
  Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.<n>This gap stems from a shortage of high-quality, action-aligned reasoning data.<n>We present GUI-Libra, a tailored training recipe that addresses these challenges.
  arXiv  Detail & Related papers  (2026-02-25T18:34:57Z)
• AutoGraphAD: A novel approach using Variational Graph Autoencoders for anomalous network flow detection [2.4159082914715495]
  AutoGraphAD is an unsupervised anomaly detection approach based on a Heterogeneous Variational Graph Autoencoder.<n>It operates on heterogeneous graphs, made from connection and IP nodes that capture network activity within a time window.<n>It achieves around 1.18 orders of magnitude faster training and 1.03 orders of magnitude faster inference.
  arXiv  Detail & Related papers  (2025-11-21T10:22:00Z)
• BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services [7.024862094862467]
  BLADE is a novel unsupervised traffic anomaly detection system for web services.<n> BLADE exploits a flow autoencoder to learn a latent feature representation and calculates its reconstruction losses per flow.<n> BLADE is extensively evaluated on both the custom dataset and the CIC-IDS 2017 dataset.
  arXiv  Detail & Related papers  (2025-11-07T12:25:39Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories [9.895353254067894]
  Abnormal stop detection in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance.<n>Existing methods often assume dense sampling or regular movement patterns, limiting their applicability.<n>We propose a Sparsity-Aware (SAS) method that adaptively defines segment boundaries based on local spatial-temporal density.
  arXiv  Detail & Related papers  (2025-10-14T16:22:34Z)
• EagleEye: Attention to Unveil Malicious Event Sequences from Provenance Graphs [1.3359586871482305]
  Securing endpoints is challenging due to the evolving nature of threats and attacks. With endpoint logging systems becoming mature, provenance-graph representations enable the creation of sophisticated behavior rules. We develop and present EagleEye, a novel system that uses rich features from provenance graphs for behavior event representation.
  arXiv  Detail & Related papers  (2024-08-17T14:48:02Z)
• Self-Tuning Self-Supervised Image Anomaly Detection [46.52880911055632]
  We introduce ST-SSAD, the first unsupervised approach to end-to-end augmentation tuning for self-supervised anomaly detection.<n>We present a new unsupervised validation loss that quantifies the alignment between augmented training data and unlabeled validation data.<n>Experiments on two testbeds with semantic class anomalies and subtle industrial defects show that ST-SSAD gives significant performance gains.
  arXiv  Detail & Related papers  (2023-06-21T05:48:51Z)
• Semi-Supervised and Long-Tailed Object Detection with CascadeMatch [91.86787064083012]
  We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage detection heads with progressive confidence thresholds. We show that CascadeMatch surpasses existing state-of-the-art semi-supervised approaches in handling long-tailed object detection.
  arXiv  Detail & Related papers  (2023-05-24T07:09:25Z)
• Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis [49.910053255238566]
  Root cause analysis (RCA) can identify the root causes of system faults/failures by analyzing system monitoring data. Previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process. We propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.
  arXiv  Detail & Related papers  (2023-05-18T01:27:48Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
  Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
  arXiv  Detail & Related papers  (2021-09-20T13:40:21Z)
• DETReg: Unsupervised Pretraining with Region Priors for Object Detection [103.93533951746612]
  DETReg is a new self-supervised method that pretrains the entire object detection network. During pretraining, DETReg predicts object localizations to match the localizations from an unsupervised region proposal generator. It simultaneously aligns the corresponding feature embeddings with embeddings from a self-supervised image encoder.
  arXiv  Detail & Related papers  (2021-06-08T17:39:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.