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Paper Citation Record · LEDGER

Rethinking Issue Resolution for AI/ML Systems

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.14657.

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

pith.paper-citation-record.v1
2607.14657 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:30:41.088961Z

measured 47 of 47 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

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved47
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c3c6b0a-e14d-4f19-b68d-4ca9c5975c7f · outbound

This paper cites Decoding the issue resolution process in practice via issue report analysis: A case study of firefox,.

Rethinking Issue Resolution for AI/ML Systems Decoding the issue resolution process in practice via issue report analysis: A case study of firefox,

Reference 1

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source=pdf_text observed=2026-08-02T01:30:36.577461Z digest=sha256:5eddd5fcee579986e838b9d8983415562c8a4b9d7648d870d13536d53e42d373

Observation f6ae55df-de8d-45b6-b628-c7f993a9afd1 · outbound

This paper cites A literature review of research in bug resolution: Tasks, challenges and future directions,.

Rethinking Issue Resolution for AI/ML Systems A literature review of research in bug resolution: Tasks, challenges and future directions,

Reference 2

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source=pdf_text observed=2026-08-02T01:30:36.665983Z digest=sha256:1eefd0936f19678b178bd5ec9a1779db56825cc5df18c782064dcc79243973db

Observation f7e36fe8-20d1-4a06-9149-45bcc89bf68c · outbound

This paper cites Zeller,Why Programs Fail: A Guide to Systematic Debugging, 2nd ed.

Rethinking Issue Resolution for AI/ML Systems Zeller,Why Programs Fail: A Guide to Systematic Debugging, 2nd ed

Reference 3

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source=pdf_text observed=2026-08-02T01:30:36.758949Z digest=sha256:8bd7483470501303b61dd74e31243ed6ef7c2bd659b677e0d20e6af1e9d0bfe9

Observation 24f3f142-6e7f-4217-83d7-ed7d861d2c99 · outbound

This paper cites Software engineering for machine learning: A case study,.

Rethinking Issue Resolution for AI/ML Systems Software engineering for machine learning: A case study,

Reference 4

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source=pdf_text observed=2026-08-02T01:30:36.826004Z digest=sha256:417c8de28f3bcfc634de5585410553d73a3bd300dc792358c2ca8d75d5338532

Observation 7feb77f8-467e-49e4-949d-37d885380623 · outbound

This paper cites Copiloting the future: How generative ai transforms software engineering,.

Rethinking Issue Resolution for AI/ML Systems Copiloting the future: How generative ai transforms software engineering,

Reference 5

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source=pdf_text observed=2026-08-02T01:30:36.929385Z digest=sha256:a39fa70b9ee2f4ef9900c3dad04f4ea49a5af6aea7fe409f9ea7f472f1674d6c

Observation bc3fc939-2fe0-494c-aec9-13c2df825c3b · outbound

This paper cites A comprehensive study on deep learning bug characteristics,.

Rethinking Issue Resolution for AI/ML Systems A comprehensive study on deep learning bug characteristics,

Reference 6

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source=pdf_text observed=2026-08-02T01:30:37.037805Z digest=sha256:88cee109e72f78a206ea2a24b30f2f63f064a2302fefadd8b2b58bb312942d47

Observation 76e114c4-21c0-49d6-a109-1848508a7a95 · outbound

This paper cites Comparative analysis of real issues in open-source machine learning projects,.

Rethinking Issue Resolution for AI/ML Systems Comparative analysis of real issues in open-source machine learning projects,

Reference 7

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source=pdf_text observed=2026-08-02T01:30:37.139394Z digest=sha256:07cbc4facf4bb56c75d1fcb6dffef9edc0cfdb292b92e723d7b348821e5aba55

Observation ca82e55a-bdbc-44fd-bf23-33a57ab7c1b2 · outbound

This paper cites A systematic survey on debugging techniques for machine learning systems,.

Rethinking Issue Resolution for AI/ML Systems A systematic survey on debugging techniques for machine learning systems,

Reference 8

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source=pdf_text observed=2026-08-02T01:30:37.207610Z digest=sha256:1f8a78f43151d01e28d65c5dbacc03f7b2e40821b99dcb28037ac0e984a78f0a

Observation f78fe65c-8a9a-493e-881f-045edb04c454 · outbound

This paper cites Taxonomy of real faults in deep learning systems,.

Rethinking Issue Resolution for AI/ML Systems Taxonomy of real faults in deep learning systems,

Reference 9

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source=pdf_text observed=2026-08-02T01:30:37.302330Z digest=sha256:8647e24ecf71282d487c477646001a1422c9fb672a32e9a1b332cab5e736fd64

Observation ab5a4f1f-c1d4-4dcf-aa05-dd4f47615f53 · outbound

This paper cites Tensorflow: An open source machine learning framework,.

Rethinking Issue Resolution for AI/ML Systems Tensorflow: An open source machine learning framework,

Reference 10

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source=pdf_text observed=2026-08-02T01:30:37.377459Z digest=sha256:0be9db11ef5e721bea7ada9a26ba6896a7bf905a649bf06d976b1f02f3eec54a

Observation a8c32d8c-514b-46cf-824d-01afbae86738 · outbound

This paper cites scikit-learn: Machine learning in python,.

Rethinking Issue Resolution for AI/ML Systems scikit-learn: Machine learning in python,

Reference 11

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source=pdf_text observed=2026-08-02T01:30:37.447271Z digest=sha256:20b619dbfc1bfe6968131a6dfe9acc06bbb8171f69ee2d9b63a5087d901183f8

Observation c574acca-6141-48d2-acf8-0a1161aace2d · outbound

This paper cites an unresolved cited work.

Rethinking Issue Resolution for AI/ML Systems Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-02T01:30:37.541901Z digest=sha256:cc249f7e22a0341e97b8cf13277ba628a6b77745d3cb3271bfbfea48c00474dc

Observation a86f3df3-0cdd-4512-a64c-79a731d75e42 · outbound

This paper cites Autogpt: An autonomous gpt-4 experiment,.

Rethinking Issue Resolution for AI/ML Systems Autogpt: An autonomous gpt-4 experiment,

Reference 13

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Observation b9d87b4a-e99d-4d1e-b65a-c9177e355987 · outbound

This paper cites Spencer,Card sorting: Designing usable categories.

Rethinking Issue Resolution for AI/ML Systems Spencer,Card sorting: Designing usable categories

Reference 14

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source=pdf_text observed=2026-08-02T01:30:37.721842Z digest=sha256:cb551a9f46d7411395cb7c773c7d35ccfb4a9232d64f9ccfc689ad85dbd43dc5

Observation e2c6f34b-2da6-4dcd-9dec-edac88d5ae78 · outbound

This paper cites How does machine learning change software development practices?.

Rethinking Issue Resolution for AI/ML Systems How does machine learning change software development practices?

Reference 15

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Observation 46ef3c86-2878-4b10-a937-b5529218b253 · outbound

This paper cites A multivocal review of mlops practices, challenges and open issues,.

Rethinking Issue Resolution for AI/ML Systems A multivocal review of mlops practices, challenges and open issues,

Reference 16

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source=pdf_text observed=2026-08-02T01:30:37.870082Z digest=sha256:0ec0bfc746f62407b19875fd59f599ea6ff616fb5f24dbbf1f31394d73aebf1b

Observation 5742e7e4-1722-4a71-b9f8-db843f48cb77 · outbound

This paper cites Maintainability challenges in ml: A systematic literature review,.

Rethinking Issue Resolution for AI/ML Systems Maintainability challenges in ml: A systematic literature review,

Reference 17

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source=pdf_text observed=2026-08-02T01:30:37.952416Z digest=sha256:5bed6f0eb2b487095e5c73dd7cbd893eb651508b85869fdb3297f4b93a420fd5

Observation e96dc6c6-6455-4fa3-b022-a5d7d9bdb297 · outbound

This paper cites Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review.

Rethinking Issue Resolution for AI/ML Systems Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review

Reference 18

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Observation 43207d43-f55e-4a64-8867-548d9447e9af · outbound

This paper cites Quality issues in machine learning software systems,.

Rethinking Issue Resolution for AI/ML Systems Quality issues in machine learning software systems,

Reference 19

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source=pdf_text observed=2026-08-02T01:30:38.150348Z digest=sha256:69b9d521653679c5c27dff57429125400a4814f9d9bb7260d9705bd786d5429e

Observation b29bf14c-63cb-40ba-a6e8-72b121fb958d · outbound

This paper cites An empirical study of pre-trained model reuse in the hugging face deep learning model registry,.

Rethinking Issue Resolution for AI/ML Systems An empirical study of pre-trained model reuse in the hugging face deep learning model registry,

Reference 20

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Observation 29006aaa-18e5-4f6e-9830-371ee67ecb2c · outbound

This paper cites An empirical study on real bugs for machine learning programs,.

Rethinking Issue Resolution for AI/ML Systems An empirical study on real bugs for machine learning programs,

Reference 21

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source=pdf_text observed=2026-08-02T01:30:38.307002Z digest=sha256:364c7bbe0e0fb962b01bef1b0b5156f8188bcad897bd09f853f71da17c35e7a6

Observation 4fb4f406-89e4-4ef8-853f-c8214be24cea · outbound

This paper cites Rajlich,Software engineering: The current practice.

Rethinking Issue Resolution for AI/ML Systems Rajlich,Software engineering: The current practice

Reference 22

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source=pdf_text observed=2026-08-02T01:30:38.370361Z digest=sha256:6471724c457ee1db9d84a618f143cd0ef0d106c242605159390296b917df0f75

Observation f0d253f7-0f3a-450b-a99b-16712307c414 · outbound

This paper cites Zeller,Why programs fail: a guide to systematic debugging.

Rethinking Issue Resolution for AI/ML Systems Zeller,Why programs fail: a guide to systematic debugging

Reference 23

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source=pdf_text observed=2026-08-02T01:30:38.432355Z digest=sha256:086c8eab32f45ff001523644493dcaf1fb5e3efc9ba3f4924eec6e14ff2e69b3

Observation e3117a72-7497-421e-98d0-d8cb59401bf3 · outbound

This paper cites Understanding the triaging and fixing processes of long lived bugs,.

Rethinking Issue Resolution for AI/ML Systems Understanding the triaging and fixing processes of long lived bugs,

Reference 24

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source=pdf_text observed=2026-08-02T01:30:38.527356Z digest=sha256:3202f30a708ca23ade33e934f4ca2447edf5e50492a94485e6028c2229acb244

Observation 6dc4c89a-7584-4e8e-9730-994a684645ea · outbound

This paper cites Replication package,.

Rethinking Issue Resolution for AI/ML Systems Replication package,

Reference 25

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source=pdf_text observed=2026-08-02T01:30:38.621281Z digest=sha256:ae9f05e7ef6501c6ee5504af1e627edb290a25635ca05c00d52e7e8133f1d6b1

Observation 387d36f0-2d47-4bda-9f2b-a67bee979a40 · outbound

This paper cites Github api,.

Rethinking Issue Resolution for AI/ML Systems Github api,

Reference 26

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source=pdf_text observed=2026-08-02T01:30:38.679184Z digest=sha256:8c29c54c3212d87fe909fa5ebce3ca1837a4fbf004a159c03421ff247c4ff95e

Observation 23f191b8-419b-41e3-8334-f401db591bf4 · outbound

This paper cites Strauss and J.

Rethinking Issue Resolution for AI/ML Systems Strauss and J

Reference 27

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source=pdf_text observed=2026-08-02T01:30:38.766612Z digest=sha256:c4efd15d97464c3af209c0cbb18f22b93650ea09173a83255dc9e1cd7bab2caa

Observation 9cf6ff93-e625-44b3-b73a-417fcd6bf86e · outbound

This paper cites Hypothesis: Web annotation tool,.

Rethinking Issue Resolution for AI/ML Systems Hypothesis: Web annotation tool,

Reference 28

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source=pdf_text observed=2026-08-02T01:30:38.859802Z digest=sha256:4e54a34fb54bc0b102c1a264356ff35e434eef49b96f1295878126cc4ac8f34e

Observation c8093eb2-8d4a-48b5-83fd-594673c57482 · outbound

This paper cites Issue #46168,.

Rethinking Issue Resolution for AI/ML Systems Issue #46168,

Reference 29

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source=pdf_text observed=2026-08-02T01:30:38.924250Z digest=sha256:f6ffdb5519af4423fa2ea3542567123345e404310e7f1d797ac104c5919c8708

Observation 378e95bf-8b9c-48e8-998a-e073aca023b1 · outbound

This paper cites [bug] mlflow.evaluate function crash on binary classi- fication evaluation,.

Rethinking Issue Resolution for AI/ML Systems [bug] mlflow.evaluate function crash on binary classi- fication evaluation,

Reference 30

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source=pdf_text observed=2026-08-02T01:30:39.020582Z digest=sha256:445a7c7bc506f2fa15cbc95fd3dc010af8101399a17a1ad96e276794e92a4852

Observation 49e25eeb-5d16-4398-8165-bbffc68fa2e6 · outbound

This paper cites [bug] loading more runs in the experiment ui becomes very slow with a large number of rows,.

Rethinking Issue Resolution for AI/ML Systems [bug] loading more runs in the experiment ui becomes very slow with a large number of rows,

Reference 31

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source=pdf_text observed=2026-08-02T01:30:39.106337Z digest=sha256:89cd0f1918d6d4b3a949e00c0367e2f30655ab1de240f351bd13de73bf8e05a4

Observation 4ecdc7b0-19b0-4d95-bdb6-054074bca619 · outbound

This paper cites Multiworkermirroredstrategy keras example hangs #35878,.

Rethinking Issue Resolution for AI/ML Systems Multiworkermirroredstrategy keras example hangs #35878,

Reference 32

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source=pdf_text observed=2026-08-02T01:30:39.193120Z digest=sha256:fbd7b715a1ab309d278bf9fc6626b2af83084a3c67dfcb8e21d212dbf9e2aa35

Observation 93de87ce-5426-4bac-89e7-dc25e295a11d · outbound

This paper cites scikit issue #29229,.

Rethinking Issue Resolution for AI/ML Systems scikit issue #29229,

Reference 33

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source=pdf_text observed=2026-08-02T01:30:39.355006Z digest=sha256:3e2edf226f05d12de9ecee97d1c9892c8f198ea4fe3f12433142aef42720f1ec

Observation ef858876-9d5d-4c3d-a885-0149a568f8d8 · outbound

This paper cites Issue #8414: (discussed in stackoverflow context) proposed application of the pipeline subset of steps,.

Rethinking Issue Resolution for AI/ML Systems Issue #8414: (discussed in stackoverflow context) proposed application of the pipeline subset of steps,

Reference 34

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source=pdf_text observed=2026-08-02T01:30:39.466281Z digest=sha256:17141799e9ce9e9b61005ec7e5aa20dcf43226237de99d96d614ad796007ba6a

Observation afe38bce-6e2e-4130-9651-bc9ad833287f · outbound

This paper cites Non-determinism from ‘tf.data.dataset.map‘ with random ops #13932,.

Rethinking Issue Resolution for AI/ML Systems Non-determinism from ‘tf.data.dataset.map‘ with random ops #13932,

Reference 35

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source=pdf_text observed=2026-08-02T01:30:39.649738Z digest=sha256:5e0daab9a7185eb0fd73607a092c0cbca54198b9d2cfa90423c710eda80904d1

Observation 2863e3f0-8b3f-4b50-92da-98fbe342c270 · outbound

This paper cites Issue #75945,.

Rethinking Issue Resolution for AI/ML Systems Issue #75945,

Reference 36

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source=pdf_text observed=2026-08-02T01:30:39.772357Z digest=sha256:ce6fe7665f0cbd4c17735365edc53d897fabaaf5dbd10b76dcc902aa31bc44a0

Observation ca06e2a3-0551-499b-92ab-b5de94b93979 · outbound

This paper cites Groupkfold inconsistent under ties in group sizes. #29495,.

Rethinking Issue Resolution for AI/ML Systems Groupkfold inconsistent under ties in group sizes. #29495,

Reference 37

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source=pdf_text observed=2026-08-02T01:30:39.921125Z digest=sha256:beefc16bd4e59b086c1403f0d0cfa3b904a3225ec33eee963aa1c9e8a8f79792

Observation e90a08ed-6458-44b5-b0a0-2e415a4d0262 · outbound

This paper cites AutoGPT Issue #2711,.

Rethinking Issue Resolution for AI/ML Systems AutoGPT Issue #2711,

Reference 38

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source=pdf_text observed=2026-08-02T01:30:40.034612Z digest=sha256:3682dbeb03dbfb82f2dcc67c87d995e8c43c8a9b308c66f427c2cdfa31f96a83

Observation bae58739-a36e-4515-95b7-25a295e4bc8f · outbound

This paper cites an unresolved cited work.

Rethinking Issue Resolution for AI/ML Systems Unresolved cited work

Reference 39

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source=pdf_text observed=2026-08-02T01:30:40.152327Z digest=sha256:6d65affa802b9156f80b28b202fec11ab41aac55ae36cde923c53cf9cd2b68bf

Observation 70983cfc-ede7-43fc-b4a3-82a5187660fa · outbound

This paper cites Hugging Face: The AI community building the future,.

Rethinking Issue Resolution for AI/ML Systems Hugging Face: The AI community building the future,

Reference 40

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no resolver link, observed 2026-08-02T01:30:40.262492Z

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source=pdf_text observed=2026-08-02T01:30:40.262492Z digest=sha256:3f86a16fe1056ea52b7ffa3f51e3fc0a75c9d9d4487d2ab76b7c260012172007

Observation 6fa6d1b6-d4bc-49aa-bd5f-c2282902dbae · outbound

This paper cites On automated and explainable provenance of ai-generated code,.

Rethinking Issue Resolution for AI/ML Systems On automated and explainable provenance of ai-generated code,

Reference 41

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source=pdf_text observed=2026-08-02T01:30:40.392360Z digest=sha256:d0de3dea2bbd523caccbcd7a3fcc9c33b025b964cfb12974c93766f715957698

Observation ca683abb-35b7-4d59-b25a-746497fa2228 · outbound

This paper cites Ai agentic programming: A survey of techniques, challenges, and opportunities,.

Rethinking Issue Resolution for AI/ML Systems Ai agentic programming: A survey of techniques, challenges, and opportunities,

Reference 42

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source=pdf_text observed=2026-08-02T01:30:40.517672Z digest=sha256:617252e8bf73a783f4f31487e2740bb223b3cc1f85495c5e331698a18979ffb0

Observation 05048d0d-4d7e-495d-b69f-39de10f122ed · outbound

This paper cites Lon- gitudinal studies,.

Rethinking Issue Resolution for AI/ML Systems Lon- gitudinal studies,

Reference 43

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no resolver link, observed 2026-08-02T01:30:40.637145Z

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source=pdf_text observed=2026-08-02T01:30:40.637145Z digest=sha256:a5d8cc5717fecbcee84ed5a136f637c4627c26f3d278e693e04fe76c6a72edd8

Observation 924a3ed1-a33a-4079-aa56-a36adcdfc288 · outbound

This paper cites Human-in-the-loop artificial intelligence: A systematic review of concepts, methods, and applications,.

Rethinking Issue Resolution for AI/ML Systems Human-in-the-loop artificial intelligence: A systematic review of concepts, methods, and applications,

Reference 44

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no resolver link, observed 2026-08-02T01:30:40.756922Z

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source=pdf_text observed=2026-08-02T01:30:40.756922Z digest=sha256:507996e4cca6713443946919258e636723a46f1d3659d3fad1c6cc42532b6153

Observation 3a3ea7c9-3ba8-400f-a667-439d6190938f · outbound

This paper cites Cybergym: Evaluating ai agents’ real-world cybersecurity capabilities at scale,.

Rethinking Issue Resolution for AI/ML Systems Cybergym: Evaluating ai agents’ real-world cybersecurity capabilities at scale,

Reference 45

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no resolver link, observed 2026-08-02T01:30:40.885576Z

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source=pdf_text observed=2026-08-02T01:30:40.885576Z digest=sha256:dda88da8c0b2dbe2bee0a9492e7031385c30c0d3ea44bf32677a30660b197f71

Observation 6070c181-c261-446a-8375-94d9e6d89d6c · outbound

This paper cites Stochastic debugging based reliability growth models for open source software project,.

Rethinking Issue Resolution for AI/ML Systems Stochastic debugging based reliability growth models for open source software project,

Reference 46

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no resolver link, observed 2026-08-02T01:30:41.004990Z

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source=pdf_text observed=2026-08-02T01:30:41.004990Z digest=sha256:00b3ecfd60270ad192f55ef08ac18930a265f22a98bc8e040117e91cfc82d991

Observation 28c03f04-d2c2-43a4-92b2-9b662a62a7d7 · outbound

This paper cites Probabilistic models for monitoring and fault diagnosis,.

Rethinking Issue Resolution for AI/ML Systems Probabilistic models for monitoring and fault diagnosis,

Reference 47

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source=pdf_text observed=2026-08-02T01:30:41.088961Z digest=sha256:4faa5a279ee51a21447e0f4a652696afd3b4f2e97f6db8741a3592ada676106a

Pith citing papers

No inbound Pith citation observations are available.