# An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum

arXiv: https://arxiv.org/abs/2605.10718 · HTML: https://mlsanity.com/paper/2605.10718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 7 of 7)

- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence](https://mlsanity.com/paper/2508.20019/) (2025)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems](https://mlsanity.com/paper/2503.03274/) (2025)
- [Resilient by Design -- Active Inference for Distributed Continuum Intelligence](https://mlsanity.com/paper/2511.07202/) (2025)
- [Bio-inspired Agentic Self-healing Framework for Resilient Distributed Computing Continuum Systems](https://mlsanity.com/paper/2601.00339/) (2026)
- [NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum](https://mlsanity.com/paper/2603.21145/) (2026)

## Abstract (arXiv)

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.