Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model

• URL: http://arxiv.org/abs/2607.01595v1
• Date: Thu, 02 Jul 2026 01:45:30 GMT
• Title: Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
• Abstract summary: Planning-Aware Semantic self-healing engine, PASE, is a novel fault self-healing framework.<n>Our framework advances autonomous system management by unifying LLM-based reasoning with model-assisted verification and meta-learned guidance.<n> Experiments on a real-world cloud fault injection dataset demonstrate that PASE significantly outperforms state-of-the-art methods.
• Score: 9.794020127306473
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Semantic self-healing engine, a novel fault self-healing framework that reconceptualizes recovery as a neuro-symbolic program synthesis task. PASE employs an LLM as a core Plan Synthesis Engine to generate structured recovery plans from a library of semantic primitives. A Neural-Symbolic World Model verifies plan feasibility through simulation, while a Meta-Prompt Optimizer, trained via DRL, learns to generate optimal prompts that guide the LLM's planning process. This tight reason-plan-verify-adapt loop enables dynamic, context-aware recovery strategy generation beyond predefined action spaces. Experiments on a real-world cloud fault injection dataset demonstrate that PASE significantly outperforms state-of-the-art methods, reducing average system recovery time by over 40% and improving fault detection accuracy in unknown fault scenarios. Our framework advances autonomous system management by unifying LLM-based reasoning with model-assisted verification and meta-learned guidance.

Related papers

• FormuEvo: LLM-Guided Evolution for Discovering Solver-Efficient Mixed-Integer Programming Formulations [66.32852384262038]
  Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization.<n>We propose FormuEvo, an evolutionary framework for automated discovery of solver-efficient MIP formulations.<n>FormuEvo frames MIP formulation design as evolutionary optimization over the symbolic space of MIP formulations.
  arXiv  Detail & Related papers  (2026-08-24T15:01:28Z)
• CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation [4.600772894287281]
  Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding.<n>We propose CoRAL, a modular framework that enables zero-shot planning by decoupling high-level reasoning from low-level control.
  arXiv  Detail & Related papers  (2026-05-04T13:49:19Z)
• PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs [16.59846708454225]
  We propose a novel multi-agent reasoning framework, referred to as Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs (PathWise)<n>PathWise formulates a sequential decision process over an entailment graph serving as a compact, stateful memory of the search trajectory.<n> Experiments across diverse COPs show that PathWise converges faster to better generalizes, generalizes across different LLM backbones, and scales to larger problem sizes.
  arXiv  Detail & Related papers  (2026-01-28T12:34:50Z)
• Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applications [0.0]
  Large language models (LLMs) are being rapidly integrated into decision-support tools, automation, and AI-enabled software systems.<n>This paper presents a system-level taxonomy of fifteen hidden failure modes that arise in real-world LLM applications.
  arXiv  Detail & Related papers  (2025-11-25T05:19:23Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms [1.819979627431298]
  This paper proposes an intelligent log processing and automatic debug framework based on Large Language Model (LLM), named Intelligent Debugger (LLM-ID)<n> Experiments on the cloud platform log dataset show that LLM-ID improves the fault location accuracy by 16.2%, which is significantly better than the current mainstream methods.
  arXiv  Detail & Related papers  (2025-06-22T04:58:37Z)
• An Intelligent Fault Self-Healing Mechanism for Cloud AI Systems via Integration of Large Language Models and Deep Reinforcement Learning [1.1149781202731994]
  We propose an Intelligent Fault Self-Healing Mechanism (IFSHM) that integrates Large Language Model (LLM) and Deep Reinforcement Learning (DRL)<n>IFSHM aims to realize a fault recovery framework with semantic understanding and policy optimization capabilities in cloud AI systems.<n> Experimental results on the cloud fault injection platform show that compared with the existing DRL and rule methods, the IFSHM framework shortens the system recovery time by 37% with unknown fault scenarios.
  arXiv  Detail & Related papers  (2025-06-09T04:14:05Z)
• Model Hemorrhage and the Robustness Limits of Large Language Models [119.46442117681147]
  Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment.<n>We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes.
  arXiv  Detail & Related papers  (2025-03-31T10:16:03Z)
• Complex LLM Planning via Automated Heuristics Discovery [48.07520536415374]
  We consider enhancing large language models (LLMs) for complex planning tasks.<n>We propose automated inferences discovery (AutoHD), a novel approach that enables LLMs to explicitly generate functions to guide-time search.<n>Our proposed method requires no additional model training or finetuning--and the explicit definition of functions generated by the LLMs provides interpretability and insights into the reasoning process.
  arXiv  Detail & Related papers  (2025-02-26T16:52:31Z)
• Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments [50.310636905746975]
  Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process. Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. We propose self-healing machine learning (SHML) to overcome these limitations.
  arXiv  Detail & Related papers  (2024-10-31T20:05:51Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.