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Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.04623.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.04623 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T16:15:54.038500Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved33
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation ad31f359-6c1d-4ace-ba44-3bf527405898 · outbound

This paper cites Lever- aging large language models for the auto-remediation of microservice applications: An experimental study,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Lever- aging large language models for the auto-remediation of microservice applications: An experimental study,

Reference 1

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:78cdba6e454126b5f92d6bc18e2fc26ed113a6742809e4558cf6a5bdb7b31e22

Observation 4020b5e3-4b49-4d2e-988b-3fe2b458bf43 · outbound

This paper cites Llm-enhanced failure localization in microservices: Integrating multi- modal data and expert interpretation,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Llm-enhanced failure localization in microservices: Integrating multi- modal data and expert interpretation,

Reference 2

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Observation 1bd50ab5-a99a-4fe1-996f-e892865c635e · outbound

This paper cites Bench- marking microservice systems for software engineering research,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Bench- marking microservice systems for software engineering research,

Reference 3

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:043c31717bec9206d1ae7cf09f0321840c4dde07e1ac1c18f74630f47b0de54f

Observation dd243e4c-f408-4af0-9621-fe6056d7b024 · outbound

This paper cites Microhecl: High-efficient root cause localization in large- scale microservice systems,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Microhecl: High-efficient root cause localization in large- scale microservice systems,

Reference 4

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:976569ecaf2b62b99c5c1eaaf9a8fc6dfb4d7bf1a157d5cf9e89265e59fdcd42

Observation a818cb88-05d7-43b0-bb06-944adcaaca84 · outbound

This paper cites Interpretable failure localization for microservice systems based on graph autoencoder,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Interpretable failure localization for microservice systems based on graph autoencoder,

Reference 5

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:1af562f48ed6782400b424dd59a514e1dc65273271029bdcfdf379d761f8a600

Observation 3985349c-0559-45a1-aa79-10affa826b7c · outbound

This paper cites Loghub: A large collection of system log datasets for ai-driven log analytics,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Loghub: A large collection of system log datasets for ai-driven log analytics,

Reference 6

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:9d12f5553f114148a6a5431719489c80804fdd9ed4e905109807d1125a78fd6f

Observation 619ef8bc-ae8d-46df-a776-8d8c1a1d522b · outbound

This paper cites A large-scale evaluation for log parsing techniques: How far are we?.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning A large-scale evaluation for log parsing techniques: How far are we?

Reference 7

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:e6bd44336bd4fa16fecdc3f83526b3f9be010456f20d75e319ae6c0974efc44b

Observation ff416295-adf7-41f3-9c85-1abc9bf7d568 · outbound

This paper cites Logeval: A comprehensive benchmark suite for llms in log analysis,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Logeval: A comprehensive benchmark suite for llms in log analysis,

Reference 8

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:2b399417ba8eca77fb43a818965828c19c1b44ce9e433aff63c8d8dae0ceb38f

Observation 4385e56e-1659-4212-a29f-423ba1315a04 · outbound

This paper cites Mrca: Metric-level root cause analysis for microservices via multi-modal data,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Mrca: Metric-level root cause analysis for microservices via multi-modal data,

Reference 9

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:1450d7783b183713f59768c85d5da6aeba9991f3f88a6ef94615a4b81f1deb9c

Observation 665520ec-bca7-4508-b25b-2fa8d84ab04c · outbound

This paper cites Rcaeval: a bench- mark for root cause analysis of microservice systems with telemetry data,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Rcaeval: a bench- mark for root cause analysis of microservice systems with telemetry data,

Reference 10

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:0baf2099377193a5b7edda0a6b11ab298c0ec05fc1f64baba1fe9f0c48c4ee2d

Observation 191e5657-7933-4e28-8069-3670ae6ae957 · outbound

This paper cites Logsage: An llm-based framework for ci/cd failure detection and remediation with industrial validation,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Logsage: An llm-based framework for ci/cd failure detection and remediation with industrial validation,

Reference 11

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:ac7a6f839bcfbe2a3c0e220add8963fdec1aabac0adfb9c4eec0e337dd1d6ea5

Observation 684f19db-af05-4390-9f74-47b14c8c524e · outbound

This paper cites Logsieve: Task-aware ci log reduction for sustainable llm-based analysis,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Logsieve: Task-aware ci log reduction for sustainable llm-based analysis,

Reference 12

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:0bf73a77846618696bea8168a1c7be9f4ed35009c8bd27ae4e5f37b8b431c2de

Observation d3b31e16-0aaa-47ea-a200-140eeeafd7d7 · outbound

This paper cites Openrca: Can large language models locate the root cause of software failures?.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Openrca: Can large language models locate the root cause of software failures?

Reference 13

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:a640843923e91c31bb4153b30db71eec69772afb15106a10b0c4e4cd0b435b57

Observation 1445d0f1-8901-4b08-9aa7-795801995d91 · outbound

This paper cites RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models

Reference 14

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Observation 2897dda8-3468-4908-9f63-25f5a5ea2baa · outbound

This paper cites Automatic root cause analysis via large language models for cloud incidents,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Automatic root cause analysis via large language models for cloud incidents,

Reference 15

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:92085f76a364a3914ff3bca5a546743baf69dfdae690fc845a25fa2afcb0f869

Observation 6ef04646-3cb1-4bcb-925e-6b576381a224 · outbound

This paper cites A mape-k approach to autonomic microservices,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning A mape-k approach to autonomic microservices,

Reference 16

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:8fce269bad11c98f06b0e8fe3593c16a41187a8b95065b9ae26a33d9221133b3

Observation 4fb06bd2-6e87-49f4-a2e7-e67e5ab7468b · outbound

This paper cites Microremed: Benchmarking llms in microservices remediation,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Microremed: Benchmarking llms in microservices remediation,

Reference 17

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Observation a71fa756-a0f5-49a3-ad03-bc97e6238193 · outbound

This paper cites Swe-bench: Can language models resolve real-world github issues?.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Swe-bench: Can language models resolve real-world github issues?

Reference 18

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Observation 0a37aba2-a156-4ffb-9333-ae057c4fadd3 · outbound

This paper cites Secbench. js: An executable security benchmark suite for server-side javascript,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Secbench. js: An executable security benchmark suite for server-side javascript,

Reference 19

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Observation c4c50947-8595-4575-a8fc-bf72a930f737 · outbound

This paper cites Root cause analysis for microservice system based on causal inference: How far are we?.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Root cause analysis for microservice system based on causal inference: How far are we?

Reference 20

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:08a68190209ba4eb4f51529b3db4760a7da1c34a715d837df9a61ec2d17a0d03

Observation 2023dfd8-3d88-4277-8c54-b2791b097fb6 · outbound

This paper cites PyRCA: A Library for Metric-based Root Cause Analysis.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning PyRCA: A Library for Metric-based Root Cause Analysis

Reference 21

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Observation 2c87c0b9-498a-48a2-afa4-f15fb41cd74f · outbound

This paper cites Logprompt: Prompt engineering towards zero-shot and interpretable log analysis,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Logprompt: Prompt engineering towards zero-shot and interpretable log analysis,

Reference 22

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Observation 3ad1d6d8-9bfb-47af-8c7d-8e9c5ef403c2 · outbound

This paper cites Leveraging rag-enhanced large language model for semi-supervised log anomaly detection,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Leveraging rag-enhanced large language model for semi-supervised log anomaly detection,

Reference 23

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:ae6ec00e9539f5bab4872b8a2bcec11b948aa1f0a0f2835771ba44c7eb62375a

Observation 6c61934a-8885-4200-be47-a7c39d16c86a · outbound

This paper cites Rcaflow: A workflow-informed hierarchi- cal planning multi-agent system for root cause analysis,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Rcaflow: A workflow-informed hierarchi- cal planning multi-agent system for root cause analysis,

Reference 24

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Observation 4cf4a668-775b-4fc6-9c2c-5c55e4bea3d7 · outbound

This paper cites Grace: A strategic llm-enhanced graph reinforcement learning framework for adaptive fault recovery in microservice systems,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Grace: A strategic llm-enhanced graph reinforcement learning framework for adaptive fault recovery in microservice systems,

Reference 25

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:4cd11528f4b19fedd32a3f8152e3cf249bb3e95026d48834b1d62e15f734d948

Observation 443b85ce-a46c-477a-a11d-ba405f61c8be · outbound

This paper cites Recommending root-cause and mitigation steps for cloud incidents using large language models,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Recommending root-cause and mitigation steps for cloud incidents using large language models,

Reference 26

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Observation 84e7ab29-6f39-4ace-a84b-7ae3a7ab4408 · outbound

This paper cites GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation

Reference 27

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Observation 34ac405b-f1a3-4475-9d20-132364474c06 · outbound

This paper cites Galr: Graph-based root cause localization and llm-assisted recovery for microservice systems,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Galr: Graph-based root cause localization and llm-assisted recovery for microservice systems,

Reference 28

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Observation a4d21170-de49-4f01-a567-0343cfabb57f · outbound

This paper cites Logformer: A pre-train and tuning pipeline for log anomaly detection,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Logformer: A pre-train and tuning pipeline for log anomaly detection,

Reference 29

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Observation 0229064e-6fac-4a3e-854d-70a8350f81c4 · outbound

This paper cites Online boutique,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Online boutique,

Reference 30

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:a410359c4a93fca437035b2c99f68f4bdfa2c1de7900557e3eb502bcef0f946c

Observation 33dbf3bb-2497-48c2-90ef-fa467cb62633 · outbound

This paper cites Kubernetes documentation,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Kubernetes documentation,

Reference 31

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:ce74a404b86f0d1c4e3ee46b7907d00f6e19daf94a65e7abb625c22759079a7c

Observation dea7b2fe-d7d7-41b6-99ce-0883ac86c339 · outbound

This paper cites Prometheus monitoring system,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Prometheus monitoring system,

Reference 32

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Observation 01486f87-3a1a-4d3f-b22c-5e8a76b09bba · outbound

This paper cites Chaos mesh documentation,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Chaos mesh documentation,

Reference 33

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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:f05662542f5453b9e7bae3a01584f6108fb38dc3aba57ba271ba61c1a8665af1

Observation 341f0b26-689d-4c79-86d6-0de975137a07 · outbound

This paper cites Onelog: towards end-to-end software log anomaly detection,.

Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning Onelog: towards end-to-end software log anomaly detection,

Reference 34

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Pith citing papers

No inbound Pith citation observations are available.