Can Large Language Models Generate Observability-Aware Code? | Paper Citation Record

Observation 80f49e1f-6e33-46a7-8555-6418a6d51047 · outbound

This paper cites Microsoft copilot,.

Can Large Language Models Generate Observability-Aware Code? Microsoft copilot,

Reference 1

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Observation 2059a4c2-0d9e-4e3f-bed8-cdc5f3c9a04b · outbound

This paper cites an unresolved cited work.

Can Large Language Models Generate Observability-Aware Code? Unresolved cited work

Reference 2

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Observation a5dd2f1b-b213-4b22-af97-ae3d87bb51ae · outbound

This paper cites Claude code,.

Can Large Language Models Generate Observability-Aware Code? Claude code,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.632008Z

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Observation bd44e3bc-6f6e-4f5a-b7aa-3060763b19e5 · outbound

This paper cites opentelemetry-demo,.

Can Large Language Models Generate Observability-Aware Code? opentelemetry-demo,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.660799Z

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Observation 61a825e1-b98f-4fbf-a869-744ea1a46583 · outbound

This paper cites microservices-demo,.

Can Large Language Models Generate Observability-Aware Code? microservices-demo,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.672782Z

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Observation fb149ceb-bd35-41c9-bcb5-63acde2d8056 · outbound

This paper cites Deathstarbench,.

Can Large Language Models Generate Observability-Aware Code? Deathstarbench,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.610772Z

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Observation f7f3624b-30c2-4fe5-95eb-15542e72b24a · outbound

This paper cites Available: https://github.com/dotnet/ eShop.

Can Large Language Models Generate Observability-Aware Code? Available: https://github.com/dotnet/ eShop

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.667561Z

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Observation 1371f726-a531-4804-a5fe-49071d667ab0 · outbound

This paper cites Available: https://github.com/ golevelup/nestjs.

Can Large Language Models Generate Observability-Aware Code? Available: https://github.com/ golevelup/nestjs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.677869Z

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Observation a966bdf4-4449-48eb-96bb-c348b8990748 · outbound

This paper cites robusta,.

Can Large Language Models Generate Observability-Aware Code? robusta,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.622471Z

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Observation 1c057c03-11e7-4969-906a-5a5ed88d876e · outbound

This paper cites microservices-demo,.

Can Large Language Models Generate Observability-Aware Code? microservices-demo,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.635412Z

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Observation aa0bc56e-8e5e-4dd0-a0be-289f6c993d1f · outbound

This paper cites train-ticket,.

Can Large Language Models Generate Observability-Aware Code? train-ticket,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.624774Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d5766d62-4793-4c6f-bc5c-15ddeb11d1bc · outbound

This paper cites Available: https://github.com/strapi/ strapi.

Can Large Language Models Generate Observability-Aware Code? Available: https://github.com/strapi/ strapi

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.630390Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ecb2555-c7bc-468a-a023-55d534ad11ba · outbound

This paper cites Available: https://github.com/ vectordotdev/vector.

Can Large Language Models Generate Observability-Aware Code? Available: https://github.com/ vectordotdev/vector

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.681251Z

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Observation cac1c754-cc8f-497c-b79e-a2d2561d346e · outbound

This paper cites Chaos mesh,.

Can Large Language Models Generate Observability-Aware Code? Chaos mesh,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.618477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5769b01-4412-4c5a-a50b-e6e510ec3f8a · outbound

This paper cites Logkg: Log failure diagnosis through knowledge graph,.

Can Large Language Models Generate Observability-Aware Code? Logkg: Log failure diagnosis through knowledge graph,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.642866Z

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Observation 78e6d05c-a01e-46c7-917c-b437ac59125b · outbound

This paper cites Faultprofit: Hierarchical fault profiling of incident tickets in large-scale cloud systems,.

Can Large Language Models Generate Observability-Aware Code? Faultprofit: Hierarchical fault profiling of incident tickets in large-scale cloud systems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.669240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 644e9718-171a-47b6-8aa5-b3cc4be7d5cf · outbound

This paper cites Fault localization for microservice applications with system logs and monitoring metrics,.

Can Large Language Models Generate Observability-Aware Code? Fault localization for microservice applications with system logs and monitoring metrics,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.653662Z

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Observation fd35283c-2116-4270-be88-bbddf927715f · outbound

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

Can Large Language Models Generate Observability-Aware Code? Automatic root cause analysis via large language models for cloud incidents,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.644645Z

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Observation f956360e-bdf7-4613-9eb1-494fb90d41a3 · outbound

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

Can Large Language Models Generate Observability-Aware Code? Microhecl: High-efficient root cause localization in large- scale microservice systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.648257Z

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Observation fa074f5b-6019-450a-b9e1-bd1677c9f146 · outbound

This paper cites Latent error prediction and fault localization for microservice applications by learning from system trace logs,.

Can Large Language Models Generate Observability-Aware Code? Latent error prediction and fault localization for microservice applications by learning from system trace logs,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.650226Z

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Observation 3d50f8ef-6b94-4fdb-af99-363bfcbef2f2 · outbound

This paper cites Openai official website,.

Can Large Language Models Generate Observability-Aware Code? Openai official website,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.676156Z

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Observation d035bfdb-14f2-4e2d-9299-eaf0ff4b44da · outbound

This paper cites an unresolved cited work.

Can Large Language Models Generate Observability-Aware Code? Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:25:48.633682Z

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Observation e6632f4a-5643-4ede-8811-e1a13bde0ca5 · outbound

This paper cites DeepMind, “Gemini,” https://deepmind.google/technologies/gemini/, 2026.

Can Large Language Models Generate Observability-Aware Code? DeepMind, “Gemini,” https://deepmind.google/technologies/gemini/, 2026

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.646416Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dc34a880-adef-4428-aff0-9e8e111ff3c0 · outbound

This paper cites Copilot sdk,.

Can Large Language Models Generate Observability-Aware Code? Copilot sdk,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.628667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 33e0788b-f925-4956-ae73-de9e20d092c8 · outbound

This paper cites LogLM: From Task-based to Instruction-based Automated Log Analysis.

Can Large Language Models Generate Observability-Aware Code? LogLM: From Task-based to Instruction-based Automated Log Analysis

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.463012Z

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Observation 00640410-a3fe-44ae-995a-3d5a007daa5c · outbound

This paper cites Truth-o-meter: Handling multiple inconsistent sources repairing llm hallucinations,.

Can Large Language Models Generate Observability-Aware Code? Truth-o-meter: Handling multiple inconsistent sources repairing llm hallucinations,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.639200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9b2725b-4377-4742-aba3-71dcc93be8f9 · outbound

This paper cites Fuzz4all: Universal fuzzing with large language models,.

Can Large Language Models Generate Observability-Aware Code? Fuzz4all: Universal fuzzing with large language models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.674503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1dac1a3b-1bcc-463b-882c-3aeff8aadcd2 · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code,.

Can Large Language Models Generate Observability-Aware Code? Livecodebench: Holistic and contamination free evaluation of large language models for code,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.671196Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91ed36c6-8927-41c0-a8e4-c6d44afc9bca · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Can Large Language Models Generate Observability-Aware Code? LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T00:25:48.477928Z

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Observation 8eb62867-bb0f-4f4f-8b94-b214403dfd50 · outbound

This paper cites arXiv preprint arXiv:2509.22237 , year=.

Can Large Language Models Generate Observability-Aware Code? arXiv preprint arXiv:2509.22237 , year=

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T00:25:48.469626Z

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Observation b5659adf-bc9c-483b-b1ff-4be568adc85d · outbound

This paper cites Program Synthesis with Large Language Models.

Can Large Language Models Generate Observability-Aware Code? Program Synthesis with Large Language Models

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.465666Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 179aafa3-dcbb-4ca6-bc4a-8c822c689c81 · outbound

This paper cites DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories.

Can Large Language Models Generate Observability-Aware Code? DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.480418Z

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Observation 47344f10-fe54-4122-82c3-c2b8a674b606 · outbound

This paper cites Codereval: A benchmark of pragmatic code generation with generative pre-trained models,.

Can Large Language Models Generate Observability-Aware Code? Codereval: A benchmark of pragmatic code generation with generative pre-trained models,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.659225Z

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Observation b24ead9d-514a-400a-a0b0-610d051e8955 · outbound

This paper cites Available: https://doi.org/10.1145/3597503.3623316.

Can Large Language Models Generate Observability-Aware Code? Available: https://doi.org/10.1145/3597503.3623316

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T00:25:48.374460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2b1b454-4246-4853-8243-65fad64404d3 · outbound

This paper cites Evaluating large language models trained on code,.

Can Large Language Models Generate Observability-Aware Code? Evaluating large language models trained on code,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.662552Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae4bb651-208a-430c-9772-7236e371c3e5 · outbound

This paper cites Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces.

Can Large Language Models Generate Observability-Aware Code? Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.472435Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ceb3b28f-b5b8-402e-85f7-fa245b9d482d · outbound

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

Can Large Language Models Generate Observability-Aware Code? Swe-bench: Can language models resolve real-world github issues?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.665883Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3240ed69-1ccf-4299-8a10-084f85ad7903 · outbound

This paper cites Where do developers log? an empirical study on logging practices in industry.

Can Large Language Models Generate Observability-Aware Code? Where do developers log? an empirical study on logging practices in industry

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-09T00:25:48.364306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1c5c037a-e60b-4b56-8eda-1243bb315243 · outbound

This paper cites Observability in microservices: An in-depth exploration of frameworks, challenges, and deployment paradigms,.

Can Large Language Models Generate Observability-Aware Code? Observability in microservices: An in-depth exploration of frameworks, challenges, and deployment paradigms,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.679568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 03a6466a-a48a-4b08-a1ff-22853c88e329 · outbound

This paper cites Drain: An online log parsing approach with fixed depth tree.

Can Large Language Models Generate Observability-Aware Code? Drain: An online log parsing approach with fixed depth tree

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.664190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28d3a693-348e-464b-b768-05bf64ac0062 · outbound

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

Can Large Language Models Generate Observability-Aware Code? A large-scale evaluation for log parsing techniques: How far are we?

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.657170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a1653982-8fad-425b-93d3-32e0a679ee77 · outbound

This paper cites LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing.

Can Large Language Models Generate Observability-Aware Code? LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-09T00:25:48.370885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3db5f66a-88fd-4b06-93af-71687302bf8a · outbound

This paper cites LogLLM: Log-based Anomaly Detection Using Large Language Models.

Can Large Language Models Generate Observability-Aware Code? LogLLM: Log-based Anomaly Detection Using Large Language Models

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.474902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5531bb97-0415-487f-b510-50e80244560f · outbound

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

Can Large Language Models Generate Observability-Aware Code? Logprompt: Prompt engineering towards zero-shot and interpretable log analysis,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.612678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11cf3db4-5800-4173-82e2-deafaf24ba47 · outbound

This paper cites Raglog: Log anomaly detection using retrieval augmented generation,.

Can Large Language Models Generate Observability-Aware Code? Raglog: Log anomaly detection using retrieval augmented generation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.614591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 539a394d-32cd-4d6b-b5d5-07d170dbc816 · outbound

This paper cites Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults,.

Can Large Language Models Generate Observability-Aware Code? Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.605008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f022a8b1-7c0f-466a-8520-c1796053f61c · outbound

This paper cites Log clustering based problem identification for online service systems,.

Can Large Language Models Generate Observability-Aware Code? Log clustering based problem identification for online service systems,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.651993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 177a1779-39af-4d70-aca8-201125674e43 · outbound

This paper cites Evlog: Identifying anomalous logs over software evolution,.

Can Large Language Models Generate Observability-Aware Code? Evlog: Identifying anomalous logs over software evolution,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.627102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cce776ca-88f2-4e13-bf4e-ee655f7a75d0 · outbound

This paper cites Mining invariants from console logs for system problem detection.

Can Large Language Models Generate Observability-Aware Code? Mining invariants from console logs for system problem detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.608973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29fc31da-3dd0-4112-8dc2-4d482e4fcccb · outbound

This paper cites Deeplog: Anomaly detection and diagnosis from system logs through deep learning,.

Can Large Language Models Generate Observability-Aware Code? Deeplog: Anomaly detection and diagnosis from system logs through deep learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.641054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aaad2218-7f16-4f5c-9904-87d4310908f8 · outbound

This paper cites Anomaly detection in oper- ating system logs with deep learning-based sentiment analysis,.

Can Large Language Models Generate Observability-Aware Code? Anomaly detection in oper- ating system logs with deep learning-based sentiment analysis,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.607013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 95b9169e-f34b-4d0b-86d4-ba6503bc5786 · outbound

This paper cites Plelog: Semi-supervised log-based anomaly detection via probabilistic label estimation,.

Can Large Language Models Generate Observability-Aware Code? Plelog: Semi-supervised log-based anomaly detection via probabilistic label estimation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.637355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 45e02c37-5003-4884-8361-fca7c63bd7ac · outbound

This paper cites Eadro: An end- to-end troubleshooting framework for microservices on multi-source data,.

Can Large Language Models Generate Observability-Aware Code? Eadro: An end- to-end troubleshooting framework for microservices on multi-source data,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.616526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f35c256e-1486-40e5-9b3e-c5d98dd5b628 · outbound

This paper cites Deeptralog: Trace-log combined microservice anomaly de- tection through graph-based deep learning.

Can Large Language Models Generate Observability-Aware Code? Deeptralog: Trace-log combined microservice anomaly de- tection through graph-based deep learning

Reference 54

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Observation df46f53b-5c68-47c4-a407-54672a53a204 · outbound

This paper cites Localizing failure root causes in a microservice through causality inference,.

Can Large Language Models Generate Observability-Aware Code? Localizing failure root causes in a microservice through causality inference,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:25:48.620246Z

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