Paper Citation Record · LEDGER

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge

As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2504.19730.

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

pith.paper-citation-record.v1
2504.19730 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:50:50.739046Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:51:08.245901Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:51:08.543165Z

Reference resolution

51 of 51 outbound references displayed

verified exact6
verified fuzzy5
unresolved38
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5910442d-a128-41ad-aba8-9a5d0089c5b8 · outbound

This paper cites In: Cohn, T., He, Y., Liu, Y.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Cohn, T., He, Y., Liu, Y

Reference 1

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Observation 11ef3996-ef8b-45e3-80d9-7cf705a82d26 · outbound

This paper cites In: Toutanova, K., Rumshisky, A., Zettle- moyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Toutanova, K., Rumshisky, A., Zettle- moyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y

Reference 2

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Observation d35cc029-a167-4605-9f7a-ffc32308251f · outbound

This paper cites In: Vanschoren, J., Yeung, S.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Vanschoren, J., Yeung, S

Reference 3

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Observation 6b29fd15-5844-44c7-820d-22e66fb85d18 · outbound

This paper cites StarCoder: may the source be with you!.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge StarCoder: may the source be with you!

Reference 4

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Observation ae760188-ffdf-4d73-808e-a440bc2ac88e · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Code Llama: Open Foundation Models for Code

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.579347Z

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Unavailable: canonical work link unavailable.

Observation 6de335e1-f5dd-4b72-a823-246d39b23f00 · outbound

This paper cites In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.582723Z

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Unavailable: canonical work link unavailable.

Observation 88afb7bb-07a3-4725-9e4d-3249cc9195ca · outbound

This paper cites In: Erk, K., Smith, N.A.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Erk, K., Smith, N.A

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.586207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddd48223-1190-4287-95ac-c608e7090544 · outbound

This paper cites In: Proceedings of the ACM/IEEE 42nd Interna- tional Conference on Software Engineering.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceedings of the ACM/IEEE 42nd Interna- tional Conference on Software Engineering

Reference 8

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Unavailable: canonical work link unavailable.

Observation 225a81b6-81c6-49ee-977c-e3f8503a0727 · outbound

This paper cites ACM Trans.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge ACM Trans

Reference 9

Resolution: verified exact; doi, observed 2026-08-16T05:50:50.879060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48cbbd7d-ba97-4e86-ae05-b654fcea6330 · outbound

This paper cites Pro- ceedings of the AAAI Conference on Artificial Intelligence 34(01), 1169–1176 (Apr 2020).

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Pro- ceedings of the AAAI Conference on Artificial Intelligence 34(01), 1169–1176 (Apr 2020)

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5dfcbd1-e1e1-4ad2-a59d-ea47491918f3 · outbound

This paper cites an unresolved cited work.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Unresolved cited work

Reference 11

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Observation 304ef482-95d9-4bf3-8977-b8bb70eb8d3d · outbound

This paper cites In: 2008 15th Working Conference on Reverse Engineering.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: 2008 15th Working Conference on Reverse Engineering

Reference 13

Resolution: verified exact; doi, observed 2026-08-16T05:50:50.849122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d7a21b5d-6409-4427-86b2-aa2996355938 · outbound

This paper cites In: 2012 34th International Conference on Software Engineering (ICSE).

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: 2012 34th International Conference on Software Engineering (ICSE)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.613575Z

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Observation d4571c85-a4d6-4345-99d9-501dc221af09 · outbound

This paper cites GPT-4 Technical Report.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge GPT-4 Technical Report

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.616873Z

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Observation 5d824287-33a7-4077-a42d-4e4c5343ab3e · outbound

This paper cites In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.620500Z

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Unavailable: canonical work link unavailable.

Observation 37b485e1-5a76-4ba1-a027-4508c409a8e8 · outbound

This paper cites CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences

Reference 18

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Unavailable: canonical work link unavailable.

Observation fffbd3d3-0e42-4286-b61d-df14f6face8f · outbound

This paper cites CodeJudge: Evaluating Code Generation with Large Language Models.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge CodeJudge: Evaluating Code Generation with Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.627403Z

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Observation f87008b5-7edf-4628-ac07-246267c3c0bd · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Training Language Models to Self-Correct via Reinforcement Learning

Reference 20

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Observation 914f75fb-488e-4442-a764-83e1c4484767 · outbound

This paper cites TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.634416Z

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Observation b262158e-ca90-4741-a2d3-806685848c5a · outbound

This paper cites In: Proceedings of the ACM/IEEE 42nd Interna- tional Conference on Software Engineering: New Ideas and Emerging Results.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceedings of the ACM/IEEE 42nd Interna- tional Conference on Software Engineering: New Ideas and Emerging Results

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.637566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4588c231-6815-4222-86b4-5e546949c6f5 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence 37(12), 14892–14900 (Jun 2023).

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Proceedings of the AAAI Conference on Artificial Intelligence 37(12), 14892–14900 (Jun 2023)

Reference 23

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Observation 1f44327c-3ea1-49e6-a5be-5cef7313cce0 · outbound

This paper cites In: Rogers, A., Boyd-Graber, J., Okazaki, N.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Rogers, A., Boyd-Graber, J., Okazaki, N

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.644595Z

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Unavailable: canonical work link unavailable.

Observation f4bbacae-280e-45c9-9675-5f0735ee646d · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: 2023 38th IEEE/ACM International Confer- ence on Automated Software Engineering (ASE)

Reference 25

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Unavailable: canonical work link unavailable.

Observation f8cbea9b-37c4-4590-ace7-6c7e7fac8010 · outbound

This paper cites Information and Software Technology135, 106552 (2021).

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Information and Software Technology135, 106552 (2021)

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.650966Z

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Unavailable: canonical work link unavailable.

Observation 184721fa-9c4a-43cb-adad-93f50e4a48df · outbound

This paper cites In: Proceedings of the 44th International Conference on Software Engineering.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceedings of the 44th International Conference on Software Engineering

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.654449Z

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Observation f3596b82-94b9-4d66-86e8-7800a1124b6f · outbound

This paper cites In: Bouamor, H., Pino, J., Bali, K.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Bouamor, H., Pino, J., Bali, K

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.657399Z

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Observation 1b1367fc-8227-4987-89b6-41579602206f · outbound

This paper cites In: Proceed- ings of the 31st ACM SIGSOFT International Symposium on Software Test- ing and Analysis.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Proceed- ings of the 31st ACM SIGSOFT International Symposium on Software Test- ing and Analysis

Reference 29

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Observation 93baa76e-e1e1-46a5-b871-701197754235 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge naturalness

Reference 30

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Observation 52276a5b-5699-44c7-8dd1-3f9d44d9cd5a · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Proceedings of the ACM on Programming Languages 8(OOPSLA2), 2355–2377 (2024)

Reference 31

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Observation b4f3bf5d-732d-4b54-bb7b-d15e9279abbb · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge ACM Trans

Reference 32

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Observation 74c80adf-220f-42dd-9a5b-a12dfdbce2a5 · outbound

This paper cites IEEE Transactions on Knowledge and Data Engineering35(3), 3159–3179 (2023).https://doi.org/10.1109/TKDE.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge IEEE Transactions on Knowledge and Data Engineering35(3), 3159–3179 (2023).https://doi.org/10.1109/TKDE

Reference 33

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Observation a2245d5d-081a-44c4-aad4-f9a9545a02b4 · outbound

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Reference 34

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Observation 6938233e-9f1b-4b49-8f72-087e66d9457b · outbound

This paper cites an unresolved cited work.

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Observation 2b79d1b2-1460-4e3e-9b9b-38d2a4f49582 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Bouamor, H., Pino, J., Bali, K

Reference 36

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Observation 79e7c925-032d-4336-8486-7bad02f323a8 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Ku, L.W., Martins, A., Srikumar, V

Reference 37

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Observation d60d82e0-a386-4c5c-b6fe-83fdea9e63c3 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

Reference 38

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?

Reference 39

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Proceedings of the AAAI Conference on Artificial Intelligence30(1) (Feb 2016)

Reference 40

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A

Reference 41

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge A Survey on Knowledge Distillation of Large Language Models

Reference 42

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Reference 43

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Qwen2.5-Coder Technical Report

Reference 44

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: 2014 IEEE International Conference on Software Maintenance and Evolution

Reference 45

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.714395Z

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge The Journal of Chemi- cal Physics 21(6), 1087–1092 (06 1953)

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.717983Z

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Observation b54785ae-83f9-4b19-ad11-ef234e6c9484 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Liu, Q., Schlangen, D

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.721679Z

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Observation 8308c0bb-24d7-47fb-ae23-06153aa8afa4 · outbound

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Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Unresolved cited work

Reference 49

Resolution: verified exact; doi, observed 2026-08-16T05:50:50.781952Z

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Observation 65e003a0-6e35-4ec5-b70f-65ebef72e706 · outbound

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

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.728137Z

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Observation 75e508bf-a31d-4bdf-ad07-49c5867cd86c · outbound

This paper cites In: Bouamor, H., Pino, J., Bali, K.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Bouamor, H., Pino, J., Bali, K

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T05:50:50.732047Z

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Observation 7db143d5-9c5d-489e-9477-6d197d473cca · outbound

This paper cites Behavioral Ecology 17(4), 688– 690 (05 2006).https://doi.org/10.1093/beheco/ark016, https://doi.org/10.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge Behavioral Ecology 17(4), 688– 690 (05 2006).https://doi.org/10.1093/beheco/ark016, https://doi.org/10

Reference 52

Resolution: verified exact; doi, observed 2026-08-16T05:50:50.764747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a67ecbc-94b8-4350-bc8a-1e545712b34c · outbound

This paper cites In: Com- puter Security–ESORICS 2017: 22nd European Symposium on Research in Com- puter Security, Oslo, Norway, September 11-15, 2017, Proceedings, Part I 22.

Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge In: Com- puter Security–ESORICS 2017: 22nd European Symposium on Research in Com- puter Security, Oslo, Norway, September 11-15, 2017, Proceedings, Part I 22

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:50:51.557559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 616200a2-78ed-404f-a347-e7e31ee6f17c · inbound

Chain-of-Code Collapse: Reasoning Failures in LLMs via Adversarial Prompting in Code Generation cites this paper.

Chain-of-Code Collapse: Reasoning Failures in LLMs via Adversarial Prompting in Code Generation Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:51:08.549047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.