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Paper Citation Record · LEDGER

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair

As of 15 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2506.04987.

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

pith.paper-citation-record.v1
2506.04987 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:32:29.493622Z

measured 42 of 42 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

42 of 42 outbound references displayed

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  • unresolved22
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External citation measurements

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Outbound references

Observation d6890629-cf98-402b-a9d4-7f4ecada1735 · outbound

This paper cites In: Cybersecurity Systems for Hu- man Cognition Augmentation, Advances in Information Security, vol.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Cybersecurity Systems for Hu- man Cognition Augmentation, Advances in Information Security, vol

Reference 1

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This paper cites In: 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

Reference 2

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair Unresolved cited work

Reference 3

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This paper cites In: 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR).

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)

Reference 4

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Observation f4dd14c8-e08d-4fce-9714-21a8f34dc8b0 · outbound

This paper cites In: 8th USENIX Conference on Operating Systems Design and Implementation.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 8th USENIX Conference on Operating Systems Design and Implementation

Reference 5

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This paper cites https://doi.org/10.1109/TSE.2020.3020502.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair https://doi.org/10.1109/TSE.2020.3020502

Reference 6

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This paper cites IEEE Transactions on Software Engineering47(09), 1943–1959 (September 2021).

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair IEEE Transactions on Software Engineering47(09), 1943–1959 (September 2021)

Reference 7

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Reference 8

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Reference 9

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This paper cites In: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Reference 10

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This paper cites In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Reference 11

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This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 12

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This paper cites In: Findings of the Association for Computational Lin- guistics: EMNLP 2020.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Findings of the Association for Computational Lin- guistics: EMNLP 2020

Reference 13

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data

Reference 14

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Reference 15

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair Vulnerability Mimicking Mutants

Reference 16

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: IEEE Con- ference on Software Testing, Verification and Validation, ICST 2024, Toronto, ON, Canada, May 27-31, 2024

Reference 17

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair techrxiv preprint (Dec 2024).https:// doi.org/10.36227/techrxiv.173385946.65994728/v1, http://dx.doi.org/10

Reference 18

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair Commu- nications of the ACM62(12), 56–65 (2019).https://doi.org/10.1145/3318162

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 9th International Conference on Learning Representa- tions, ICLR 2021, Virtual Event, Austria, May 3-7, 2021

Reference 21

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: European Symposium on Research in Computer Security

Reference 22

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Guide to Vulnerability Analysis for Com- puter Networks and Systems - An Artificial Intelligence Approach, pp

Reference 23

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair 15599983

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022

Reference 25

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 34th In- ternational Conference on Software Engineering (ICSE)

Reference 28

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Proceedings of the 28th ACM SIGSOFT Interna- tional Symposium on Software Testing and Analysis

Reference 29

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 38th International Conference on Software Engineering (ICSE)

Reference 30

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 35th International Conference on Software Engineering (ICSE)

Reference 31

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Proceedings of the 21st In- ternational Conference on Mining Software Repositories

Reference 32

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A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair IEEE Trans

Reference 34

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Reference 35

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Observation 6e933980-5603-49db-b319-bc4e42eff9b8 · outbound

This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T10:32:29.477688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:32:29.477688Z digest=sha256:75ca6d848c09f108a1eeba673dfd499fb7981d4ea60df9a0c05deb4b0dc031ea

Observation 7bccbdd3-2669-42d9-b701-60d3a938a64e · outbound

This paper cites In: 33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: 33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:32:30.864955Z

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.

source=pdf_text observed=2026-08-07T10:32:29.480803Z digest=sha256:60764e24d6be2fc5e7a2d00ee061619f0810f513a3ccb2a121ea0e096fba9d4e

Observation 49f27ff5-6d56-4847-ab59-693e824f8fd8 · outbound

This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T10:32:29.484024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:32:29.484024Z digest=sha256:b9a51d3ce72e4793e0674e0dfc6edb32732214b770b564e46be0d9483ed09150

Observation 3b484133-1181-4cab-ac35-3591a5552bea · outbound

This paper cites In: Proceedings of the 32nd ACM SIGSOFT International Symposium on Soft- ware Testing and Analysis, ISSTA 2023, Seattle, WA, USA, July 17-21, 2023.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair In: Proceedings of the 32nd ACM SIGSOFT International Symposium on Soft- ware Testing and Analysis, ISSTA 2023, Seattle, WA, USA, July 17-21, 2023

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T10:32:29.487600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:32:29.487600Z digest=sha256:8859bea23e19e03cdcfcc2df4db9e3b2e8fb801cd236312899eca7cc15ca2957

Observation 66c26b7f-3ccf-444e-a530-db7d0ca5d3d2 · outbound

This paper cites IEEE Transactions on Software Engineering 43, 34–55 (2017).

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair IEEE Transactions on Software Engineering 43, 34–55 (2017)

Reference 41

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T10:32:29.751434Z

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.

source=pdf_text observed=2026-08-07T10:32:29.490642Z digest=sha256:0818a47890b79d27160f73e6d3c217b10f2f58a3d577227dd4a5668bfcfeb33f

Observation 3e57926f-9588-41eb-ab7e-853eaab6c41a · outbound

This paper cites IEEE Access 8, 166335–166346 (2020).

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair IEEE Access 8, 166335–166346 (2020)

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:32:30.853579Z

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.

source=pdf_text observed=2026-08-07T10:32:29.493622Z digest=sha256:055b7f4c382d7d48eefde42252cc19d27b68e543e4279cd941336d3b6be56e82

Observation 3c71f3e4-bce5-4ce9-9feb-9db6bb8ec8a1 · outbound

This paper cites https://doi.org/10.1007/978-3-319-10374-7_3 , https: //doi.org/10.1007/978-3-319-10374-7_3.

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair https://doi.org/10.1007/978-3-319-10374-7_3 , https: //doi.org/10.1007/978-3-319-10374-7_3

Reference 60

Resolution
verified exact
doi, observed 2026-08-07T10:32:29.622350Z

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.

source=pdf_text observed=2026-08-07T10:32:29.372767Z digest=sha256:f8650d2be6f25624d4ed7b90fbee6f7ac365b62dd7f45955a6aa81fb84632c77

Pith citing papers

No inbound Pith citation observations are available.