agent/papers/items/2026-2607-04623-can-llms-really-recover-microservice-failures-a-recovery-aware-evaluation-of-dia.md

Paper: Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning

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type: paper title: Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning authors: Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang, Shaohan Huang, Carol Fung, Yongxin Tong, Hailong Yang, Depei Qian year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.04623 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-06 updated_at: 2026-07-06 status: queued relevance: high topics:

• agent-evaluation
• planning
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.SE
• cs.DC related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: rag-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: rag-agent
• inferred topics: agent-evaluation, planning, rag, reasoning, tool-use
• arXiv categories: cs.SE, cs.DC
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.04623