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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 7 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 2026-08-07T06:34:17.273281+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

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  • 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=sha256:9077d71578b5b3093f9fe0134075bc8cd0624c59bab95bcd200b526c74027365

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:6ffd443da7db1f36197b8b9ef191221a5768680fe10ccfacf263d95d5992c5a3

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=sha256:142492dda33da6331bf3d01d99b6dabe3a3d593836edb41b3b1285a5b732db15

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=sha256:8e4cefdcedce7a5fbae6a1cf20bc8f63fa4bf59b1d9235eb46d29e237cb19066

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:bce6fa428ccfc9ac036d37d66375b2900a7ebe9c1e4edf7893caf8b58fe191a7

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:fe2406d849c311d7e97a344277c401d991284257f4f418edc41674e27a9d33fc

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=sha256:6b8e9feb2893262ab7842fc340f65622bc817c55cf7affe9a9d1cd4af42fc8d7

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=sha256:1f1556a7cdbe11b20b2e7720ad444ed265e7639af85be328328b7cf02bd3ad6e

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:d2798905dced139758175c24426cf53b80af1a650c1b4aa08b8aa36b89d7fcad

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=sha256:3aee565cd9727662cb5233fe6c98df378fe1e753e4e6631c097550862cbe1d91

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=sha256:4a2b710ae61b38174eb0786adfe6a297d1a9f78021f3f2b8f1e825fe8b561f32

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:feccec09e000eaebb676897f0fed3fa3390cc1c9957c320324445f7593ab22ff

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=sha256:168a2a19fd205e3aa46d2195e5c1c530403f8e1f4c3d17785991ceb0794acc68

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:2fe5c4224e65498adf9eedfc1af5c612ee1d36f34ff01eed40168df2907221e6

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:a1f5ba20f452902f845e26598b1cb3878275f7fd6853c971ee7abacc78b63b60

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=sha256:1a56806240d69c11c8b1f07fcf6ad1246015dccbefd1f4c2fc522d80786ed144

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:91b8a94359c7a3c7a3e1e02652f96417b5d5d410d26a5113e4f6ef364698e137

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=sha256:9690ce1f5c1c70d052f83da3fd0c5459d9f1bc7e513e58348239373fbbe64200

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:35894e25210d492b3cbf7de4dc26f19d08bdb4404e760b1b6dcc9cc83b5fb3c0

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=sha256:8007c5d3b79df8fe3b727d3e623cc8241a6d11ce7e84351d8751f478ea0e2745

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:c4937d63563daa4368c7dbf5df205f3037339dcddec07a1e204dc39ccf9685c5

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:47967aea7d5915d66b6464e6bda1a6a3a8683b90571deeee1645957d3c9a88e6

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:6c102bb52e233a290c2d3b2a4b14f9671b581190b4181e45fd96104af98802e0

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:8177742dbb7fb5ca26429f3af934ea330842d5f3a9d363d22e70353800cca374

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:a66000e515b14eb56e42529de81c493a7151d179ba8747475e1af5ebdce66426

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=sha256:8b680914dce96ec41865e08ca6a04b799787d1c28cd8572ac7bf3eb6645cb7e9

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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source=pdf_text observed=2026-07-11T16:15:54.038500Z digest=sha256:69c3757967014bb38d43c791679596e0dca778ad6cda0a0cc4f4b39bda72921b

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:dd7f84bc344e700c985de222f38c7c4cdbad4e513ee3bf6d3e29fde82c877ba8

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.