[2603.25538] Missing-Aware Multimodal Fusion for Unified Microservice Incident Management

Computer Science > Machine Learning arXiv:2603.25538 (cs) [Submitted on 26 Mar 2026] Title:Missing-Aware Multimodal Fusion for Unified Microservice Incident Management Authors:Wenzhuo Qian, Hailiang Zhao, Ziqi Wang, Zhipeng Gao, Jiayi Chen, Zhiwei Ling, Shuiguang Deng View a PDF of the paper titled Missing-Aware Multimodal Fusion for Unified Microservice Incident Management, by Wenzhuo Qian and 6 other authors View PDF HTML (experimental) Abstract:Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume perfect data completeness. In practice, network fluctuations and agent failures frequently cause missing modalities. Existing approaches relying on static placeholders introduce imputation noise that masks anomalies and degrades performance. To address this, we propose ARMOR, a robust self-supervised framework designed for missing modality scenarios. ARMOR features: (i) a modality-specific asymmetric encoder that isolates distribution disparities among metrics, logs, and traces; and (ii) a missing-aware gated fusion mechanism utilizing learnable placeholders and dynamic bias compensation to prevent cross-modal interference from incomplete inputs. By employing self-supervised auto-regression with mask-guided reconstruction, ARMOR jointly optimizes anomaly detection (AD), failure triage (FT), and root cause localization (RCL). AD and RCL require no fault...