Paper Citation Record · LEDGER

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit

As of 14 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 2 inbound Pith citation observations for arXiv:2411.10842.

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

pith.paper-citation-record.v1
2411.10842 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:19:26.565045Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:04:43.380469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:53:03.485242Z

Reference resolution

77 of 77 outbound references displayed

verified exact1
verified fuzzy36
unresolved40
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 023335db-4002-4975-9554-53b93e192ec2 · outbound

This paper cites Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.095568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecf1dcc1-1f56-495e-8b3d-c2764e071017 · outbound

This paper cites Large language models are edge-case generators: Crafting unusual programs for fuzzing deep learning libraries,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Large language models are edge-case generators: Crafting unusual programs for fuzzing deep learning libraries,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:29.156299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation daf6ddb4-bc11-490d-a56b-3372978b9b4e · outbound

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

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Fuzz4all: Universal fuzzing with large language models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:29.119482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e8956fac-76cf-4df3-a098-19ab67254503 · outbound

This paper cites Automated program repair in the era of large pre-trained language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Automated program repair in the era of large pre-trained language models,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.116613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93350e49-40f3-4045-84aa-39163f286771 · outbound

This paper cites Time Travel in LLMs: Tracing Data Contamination in Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Time Travel in LLMs: Tracing Data Contamination in Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.122323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aec18199-5022-430c-be9c-f18334480712 · outbound

This paper cites NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:29.035929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b484a52-2013-4f30-adda-d5acefe41291 · outbound

This paper cites Task Contamination: Language Models May Not Be Few-Shot Anymore.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.137171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c576fd31-1149-4b0a-a936-5a58ff3106f0 · outbound

This paper cites An Open Source Data Contamination Report for Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit An Open Source Data Contamination Report for Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.143329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b3a46d8-7c2d-4baf-b71d-0bcad4624cae · outbound

This paper cites Concerned with Data Contamination? Assessing Countermeasures in Code Language Model.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Concerned with Data Contamination? Assessing Countermeasures in Code Language Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.148289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf2ed20c-f118-4400-a3b8-dbbd726e9ade · outbound

This paper cites Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source LLMs,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source LLMs,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.999155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a29b4ed-b5a0-4a4e-8462-966f6ff92dc2 · outbound

This paper cites Memorization without overfitting: Analyzing the training dynamics of large language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Memorization without overfitting: Analyzing the training dynamics of large language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.966363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a66c0329-2cec-41b6-8a08-cbdc3767b83a · outbound

This paper cites Deduplicating training data mitigates privacy risks in language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Deduplicating training data mitigates privacy risks in language models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.904071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b7585dca-54b9-45b2-8d98-a039975f0732 · outbound

This paper cites It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.169621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0810c142-9362-487c-acd8-ef25dcf0ffa2 · outbound

This paper cites Data Contamination: From Memorization to Exploitation.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Data Contamination: From Memorization to Exploitation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.176641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde182f8-6e4d-45d2-9792-87174f3d41e5 · outbound

This paper cites Datasets for Large Language Models: A Comprehensive Survey.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Datasets for Large Language Models: A Comprehensive Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.182568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 170c7ba4-d033-4d13-9e52-6f40f66fcc99 · outbound

This paper cites Detecting Pretraining Data from Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Detecting Pretraining Data from Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.188805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf0e6f64-8ba2-4272-a239-44baaad9b4fd · outbound

This paper cites Extracting training data from large language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Extracting training data from large language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.864143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c00b709-b95d-4b4a-8ff1-01dacce75833 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.198876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4853edc5-54cf-41a4-8d7b-3f926a6bd77e · outbound

This paper cites An ethnographic study of copy and paste programming practices in oopl,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit An ethnographic study of copy and paste programming practices in oopl,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.833503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 75274934-9f53-41db-9d32-f3759fe61c64 · outbound

This paper cites Github copilot,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Github copilot,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.801411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4dfdde71-f3cc-4068-b802-46833b6fa7bd · outbound

This paper cites Github survey finds nearly all developers using ai coding tools,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Github survey finds nearly all developers using ai coding tools,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.216415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dea7ad3-cbbd-470b-b3e5-8b7ab4bb3733 · outbound

This paper cites Generalization or memorization: Data contamination and trustworthy evaluation for large language models,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Generalization or memorization: Data contamination and trustworthy evaluation for large language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.768918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0df144da-b511-4307-91e6-1026e02cb5b1 · outbound

This paper cites Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Boosting Static Resource Leak Detection via LLM-based Resource-Oriented Intention Inference

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.226880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b937deba-1425-4a4c-839e-7df0355cce19 · outbound

This paper cites Mr-adopt: Automatic deduction of input transformation function for metamorphic testing,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Mr-adopt: Automatic deduction of input transformation function for metamorphic testing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.735213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d49d0cf5-20df-4e3b-a059-b26fd4cd7013 · outbound

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

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Evaluating large language models trained on code,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.237833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a87a9a84-88e0-4321-970f-5610276d86c3 · outbound

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

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit SWE-bench: Can language models resolve real-world github issues?

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.683167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 400562a5-4b06-4464-a87d-5e112fa05651 · outbound

This paper cites On Leakage of Code Generation Evaluation Datasets.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit On Leakage of Code Generation Evaluation Datasets

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.247246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85a76b72-d987-48db-aaa3-77a4d7b45f0d · outbound

This paper cites Refactoring Programs Using Large Language Models with Few-Shot Examples.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Refactoring Programs Using Large Language Models with Few-Shot Examples

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.253196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07a887de-ed89-4d3b-ba82-9cbcd84f99db · outbound

This paper cites How effective are neural networks for fixing security vulnerabilities,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit How effective are neural networks for fixing security vulnerabilities,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.259029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04de4643-be26-407c-83f9-19ebe3d19f86 · outbound

This paper cites Exploring Multi-Lingual Bias of Large Code Models in Code Generation.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Exploring Multi-Lingual Bias of Large Code Models in Code Generation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.264412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcf8021b-256e-4701-9d0d-55d8da3cbd3d · outbound

This paper cites Perplexity—a measure of the difficulty of speech recognition tasks,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Perplexity—a measure of the difficulty of speech recognition tasks,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.271834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d9bd4ad-4cab-4306-9ce4-1b905e732451 · outbound

This paper cites The Stack.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit The Stack

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.623475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 78a6b93f-100b-4bfb-a38f-353218349291 · outbound

This paper cites Data Portraits: Recording Foundation Model Training Data.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Data Portraits: Recording Foundation Model Training Data

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.285625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c3a3010-f2c3-402b-9213-419fe217b97c · outbound

This paper cites DataPortraits.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit DataPortraits

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.597572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a12f2833-3d95-435c-ab7c-75a438acae07 · outbound

This paper cites Code of sklearn.externals. arff.LODGeneratorData class.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Code of sklearn.externals. arff.LODGeneratorData class

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.547454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eff7f8c2-715b-4f48-a81b-e62623e1cfc8 · outbound

This paper cites Kreuzer and L.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Kreuzer and L

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.508352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f44f6ef-bdd4-4303-b7c7-be95ed1d5f11 · outbound

This paper cites Semmt: a semantic-based testing approach for machine translation systems,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Semmt: a semantic-based testing approach for machine translation systems,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.483248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0d79fab7-706d-4088-b4e0-15e949e63ac0 · outbound

This paper cites Testing your question answering software via asking recursively,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Testing your question answering software via asking recursively,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.449723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f38a357a-4da1-4520-8795-602b10e5b536 · outbound

This paper cites Structure-invariant testing for machine translation,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Structure-invariant testing for machine translation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.417973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 62f7a8ff-dd66-44c5-8835-a36ea1fd1163 · outbound

This paper cites Validation on machine reading compre- hension software without annotated labels: A property-based method,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Validation on machine reading compre- hension software without annotated labels: A property-based method,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.390664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8e49d4e7-9b81-4721-b9e8-7523cafe2a43 · outbound

This paper cites Word closure-based metamorphic testing for machine translation,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Word closure-based metamorphic testing for machine translation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.365330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aaae39e2-62ed-4fed-9c53-274e594731a0 · outbound

This paper cites an unresolved cited work.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:19:28.340102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0422d2e5-bada-46e3-9667-723384fc0979 · outbound

This paper cites codellama/codellama-7b-instruct-hf,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit codellama/codellama-7b-instruct-hf,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.314337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3db0995-9290-4d1d-ac5f-4c4bf94239ad · outbound

This paper cites Starcoder-code-instruct,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Starcoder-code-instruct,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.291076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 33115363-90ce-4ffd-813b-819cbd8927ec · outbound

This paper cites The stack: 3 tb of permissively licensed source code,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit The stack: 3 tb of permissively licensed source code,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.259663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6ebd5c34-980a-459b-9f08-a7216493ab3c · outbound

This paper cites Huggingfaceh4/starchat-beta,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Huggingfaceh4/starchat-beta,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.232607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5fb57f79-3c97-433e-9375-c0c28ff4d9eb · outbound

This paper cites Wizardlm/wizardcoder,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Wizardlm/wizardcoder,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.206731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cf407ddd-6bd9-4496-bbce-cffcffa80531 · outbound

This paper cites CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.367153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a97b00a-8771-4518-b133-9e644a85620c · outbound

This paper cites Scikit-Learn Project.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Scikit-Learn Project

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.183623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation be14be25-c0a6-441f-b251-30d5bd84ba98 · outbound

This paper cites Pandas Project.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Pandas Project

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.151123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 02bfbb5f-02b9-4250-9e44-64c3036837ee · outbound

This paper cites NumPy Project.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit NumPy Project

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.122485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4ea151a6-1bec-4b8c-b819-43b3143a950e · outbound

This paper cites Investigating Data Contamination for Pre-training Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Investigating Data Contamination for Pre-training Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.389387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa66654b-602b-4b33-8f7a-4f5f1e8d0f5a · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.396819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fceabdd-9569-470c-b336-e9316c5a8668 · outbound

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

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit StarCoder: may the source be with you!

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.401865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d5f8697-fff6-44c3-9205-0f002c6259eb · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit The secret sharer: Evaluating and testing unintended memorization in neural networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.088191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 716801ea-41fe-4022-80c0-3f7a7604c1be · outbound

This paper cites Language models are few-shot learners,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Language models are few-shot learners,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:28.056931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23d99221-32d9-4862-80bc-61a7c7af6706 · outbound

This paper cites Palm: Scaling language modeling with pathways,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Palm: Scaling language modeling with pathways,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.420159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9510a7a5-53f2-440f-957e-aab03daeb324 · outbound

This paper cites The pile.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit The pile

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:27.997463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c926a424-d340-49c3-8287-d90427752b51 · outbound

This paper cites The Stack-V2.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit The Stack-V2

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:27.971739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 396bd525-a13b-4500-9158-e533453342e7 · outbound

This paper cites Investigating Data Contamination in Modern Benchmarks for Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Investigating Data Contamination in Modern Benchmarks for Large Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.436599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9de5d46d-f6f7-489b-9712-2d986141ef57 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.442574Z

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Unavailable: canonical work link unavailable.

Observation e467217f-ad2b-4a32-9587-7d6668f40eff · outbound

This paper cites Measuring Massive Multitask Language Understanding.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Measuring Massive Multitask Language Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.449798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b6d5c71-4828-4a74-aed9-dc64817b5297 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Privacy risk in machine learning: Analyzing the connection to overfitting,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:27.930181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ad0d76e5-a035-479d-993b-5ed85a40aada · outbound

This paper cites Zlib compression library,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Zlib compression library,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.474590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ccc18a-2f03-42d4-ac13-aab4a1642d12 · outbound

This paper cites Membership Inference Attacks against Language Models via Neighbourhood Comparison.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Membership Inference Attacks against Language Models via Neighbourhood Comparison

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.484724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efef95b9-ffe5-4db4-90ec-3de08df662ca · outbound

This paper cites Do Membership Inference Attacks Work on Large Language Models?.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Do Membership Inference Attacks Work on Large Language Models?

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.491701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9efa1576-106c-4170-9bed-f45f839b6fd4 · outbound

This paper cites Program Synthesis with Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Program Synthesis with Large Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.499246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e3865b1-3146-413f-8197-5b0f768f513a · outbound

This paper cites Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.505608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adccf255-7cc6-46b7-a737-cae38867b728 · outbound

This paper cites Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:27.856437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1c761014-dc7b-427b-8f24-c3319c0e84a8 · outbound

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

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.517105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8fa02bd-40b4-40cc-9852-ac01fb36b273 · outbound

This paper cites Large Language Models for Software Engineering: Survey and Open Problems.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Large Language Models for Software Engineering: Survey and Open Problems

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.525517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6d1c045-831f-409c-83fe-6b3c24dd6611 · outbound

This paper cites AI-assisted coding: Experiments with GPT-4.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit AI-assisted coding: Experiments with GPT-4

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.535637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1859ee00-1fef-444c-b274-e0e364c001dc · outbound

This paper cites Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:19:26.753852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 38cd1af4-0d46-4eb3-955f-5f9a6b3f722c · outbound

This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.551871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7daf727d-707b-4bb8-8756-62011fcbd6ef · outbound

This paper cites Measuring coding challenge competence with apps,.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Measuring coding challenge competence with apps,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.557949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdd642bf-bc89-404b-b8e5-fb7e16400bf2 · outbound

This paper cites Code Language Models trained on Stack.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Code Language Models trained on Stack

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:19:27.817579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6650bce7-b782-41ae-b321-d9a0a4beb8eb · outbound

This paper cites Time Travel in LLMs: Tracing Data Contamination in Large Language Models.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Time Travel in LLMs: Tracing Data Contamination in Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T19:19:26.127610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9456c85e-73f0-46a5-b62b-7a297af0b4cb · inbound

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation cites this paper.

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:53:03.487111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5ff6ebf3-321e-45e5-8090-e84b1f6268ff · inbound

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation cites this paper.

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:04:43.380469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.