An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
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arXiv
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| Format: | Preprint |
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2026
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| author | De Silva, Suvi Lapkovskis, Alfreds Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar |
| author_facet | De Silva, Suvi Lapkovskis, Alfreds Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar |
| contents | Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge-tier environments. The framework employs parallel micro-agents that integrate the free-energy principle, causal do-calculus, and localized causal state-graphs to support counterfactual root-cause analysis within each fault's Markov blanket. Restricting inference to causally relevant variables reduces computational overhead while preserving diagnostic fidelity. AURORA further introduces a dual-gated execution mechanism that authorizes remediation only when causal confidence is high and predicted epistemic uncertainty is bounded; otherwise, it abstains from local intervention and escalates the diagnostic payload to the fog tier. Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_10718 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum De Silva, Suvi Lapkovskis, Alfreds Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Performance Systems and Control Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge-tier environments. The framework employs parallel micro-agents that integrate the free-energy principle, causal do-calculus, and localized causal state-graphs to support counterfactual root-cause analysis within each fault's Markov blanket. Restricting inference to causally relevant variables reduces computational overhead while preserving diagnostic fidelity. AURORA further introduces a dual-gated execution mechanism that authorizes remediation only when causal confidence is high and predicted epistemic uncertainty is bounded; otherwise, it abstains from local intervention and escalates the diagnostic payload to the fog tier. Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair. |
| title | An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Performance Systems and Control |
| url | https://arxiv.org/abs/2605.10718 |