# NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum

arXiv: https://arxiv.org/abs/2603.21145 · HTML: https://mlsanity.com/paper/2603.21145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [LogSage: An LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation](https://mlsanity.com/paper/2506.03691/) (2025)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [Resilient by Design -- Active Inference for Distributed Continuum Intelligence](https://mlsanity.com/paper/2511.07202/) (2025)
- [Service Orchestration in the Computing Continuum: Structural Challenges and Vision](https://mlsanity.com/paper/2602.15794/) (2026)
- [Bio-inspired Agentic Self-healing Framework for Resilient Distributed Computing Continuum Systems](https://mlsanity.com/paper/2601.00339/) (2026)

## Built on by (most cited first, 2 of 2)

- [Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum](https://mlsanity.com/paper/2605.09623/) (2026)
- [An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum](https://mlsanity.com/paper/2605.10718/) (2026)

## Abstract (arXiv)

The computational demands of modern AI services are increasingly shifting execution beyond centralized clouds toward a computing continuum spanning edge and end devices. However, the scale, heterogeneity, and cross-layer dependencies of these environments make resilience difficult to maintain. Existing fault-management methods are often too static, fragmented, or heavy to support timely self-healing, especially under noisy logs and edge resource constraints. To address these limitations, this paper presents NeSy-Edge, a neuro-symbolic framework for trustworthy self-healing in the computing continuum. The framework follows an edge-first design, where a resource-constrained edge node performs local perception and reasoning, while a cloud model is invoked only at the final diagnosis stage. Specifically, NeSy-Edge converts raw runtime logs into structured event representations, builds a prior-constrained sparse symbolic causal graph, and integrates causal evidence with historical troubleshooting knowledge for root-cause analysis and recovery recommendation. We evaluate our work on representative Loghub datasets under multiple levels of semantic noise, considering parsing quality, causal reasoning, end-to-end diagnosis, and edge-side resource usage. The results show that NeSy-Edge remains robust even at the highest noise level, achieving up to 75% root-cause analysis accuracy and 65% end-to-end accuracy while operating within about 1500 MB of local memory.