Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
arXiv:2607.01595v1 Announce Type: cross Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive recovery has become a critical challenge. While existing approaches integrate Large Language Models (LLMs) for semantic understanding and Deep Reinforcement Learning (DRL) for policy optimization, they often rely on sequential, loosely coupled architectures that underutilize the generative and reasoning capabilities of LLMs. In this paper, we propose a paradigm shift with PASE, a Planning-Aware Sema
The increasing scale and complexity of cloud-based AI systems necessitate more robust and autonomous recovery mechanisms to maintain reliability, pushing research into integrating advanced AI for self-healing infrastructure.
This development addresses a critical vulnerability in large-scale AI deployments by enabling more resilient and adaptive cloud infrastructure, reducing downtime and operational costs associated with system failures.
Current reactive fault recovery shifts towards a proactive, LLM-driven planning and verification approach, potentially transforming how cloud AI systems are maintained and optimized.
- · Cloud providers
- · AI-reliant enterprises
- · DevOps teams
- · Manual IT operations
- · Legacy monitoring solutions
Cloud-based AI services become significantly more reliable and self-sufficient.
Reduced human intervention in cloud infrastructure management leads to a redistribution of IT roles and skills.
Increased trust in autonomous AI systems for critical functions could accelerate broader AI integration across industries.
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Read at arXiv cs.CL