mlsanityData to 7 Oct 2026

E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning

arXiv 2604.11094 ↗First posted 13 Apr 2026cs.SE cs.AIcited by 3

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Abstract (arXiv)

Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-based auto-remediation approaches have emerged, they primarily translate textual instructions into executable Ansible playbooks and rely on expert-crafted prompts, lacking runtime knowledge guidance and depending on large-scale general-purpose LLMs, which limits their accuracy and efficiency. We introduce textitEnd-to-End Microservice Remediation (E2E-MR), a new task that requires directly generating executable playbooks from diagnosis reports to autonomously restore faulty systems. To enable rigorous evaluation, we build textitMicroRemed, a benchmark that automates microservice deployment, failure injection, playbook execution, and post-repair verification. We further propose textitE2E-REME, an end-to-end auto-remediation model trained via experience-simulation reinforcement fine-tuning. Experiments on public and industrial microservice platforms, compared with nine representative LLMs, show that E2E-REME achieves superior accuracy and efficiency.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 64 references that are in the map. First posted on arXiv 13 Apr 2026.

Builds on

23 of 64 references.

References · 23

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11 more
  1. 2023Xpert: Empowering Incident Management with Query Recommendations via Large Language Modelscited by 22
  2. 2025AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agentscited by 18
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  5. 2025Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable?cited by 9
  6. 2026Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservicescited by 8
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discoverycited by 4
  2. 2026Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuningcited by 2
  3. 2026Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thoughtcited by 0