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Paper Citation Record · LEDGER

LLM Performance for Code Generation on Noisy Tasks

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.23598.

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

pith.paper-citation-record.v1
2505.23598 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:44.110888Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 32113471-a8b7-4923-9850-9af0f4475126 · outbound

This paper cites Large Language Models for Software Engineering: A Systematic Literature Review.

LLM Performance for Code Generation on Noisy Tasks Large Language Models for Software Engineering: A Systematic Literature Review

Reference 1

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source=pdf_text observed=2026-08-07T12:45:40.068254Z digest=sha256:d283359b9180726f917549b0951c55ff46ffec4b86c25c7957f8c494c4f97b8f

Observation 4f742216-4b78-4527-a46f-d1860c26499b · outbound

This paper cites The Current Challenges of Software Engineering in the Era of Large Language Models.

LLM Performance for Code Generation on Noisy Tasks The Current Challenges of Software Engineering in the Era of Large Language Models

Reference 2

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source=pdf_text observed=2026-08-07T12:45:40.184121Z digest=sha256:4a46bed51bd4f2507d5263bc8baf0982265b4c315f1d09e24c3525ff746906c2

Observation c3ecf66b-b835-4348-9251-46c8cdb2961b · outbound

This paper cites LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs.

LLM Performance for Code Generation on Noisy Tasks LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs

Reference 3

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source=pdf_text observed=2026-08-07T12:45:40.247629Z digest=sha256:90e79655c9f9aace2d3950bd535153cfb0335215ef72915bef2f82bc8fa84879

Observation 3f95a9a8-de2b-49fc-860b-37194df0a11e · outbound

This paper cites Math word problem solving on math leaderboard,.

LLM Performance for Code Generation on Noisy Tasks Math word problem solving on math leaderboard,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:40.367337Z digest=sha256:4a6c55592244114583fc12dd7ef224d08f2c6ded35c442160e683c814fd6bfb8

Observation 3b1f3e53-1f7c-4506-944b-f059231ae39c · outbound

This paper cites A performance study of llm-generated code on leetcode,.

LLM Performance for Code Generation on Noisy Tasks A performance study of llm-generated code on leetcode,

Reference 5

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source=pdf_text observed=2026-08-07T12:45:40.473842Z digest=sha256:e6b6324df055148901c134475e60db6b18238e39426c527f1eee49dfe2679919

Observation 1745ef85-11e2-42cf-b5f8-97d61dd8a9e7 · outbound

This paper cites Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research.

LLM Performance for Code Generation on Noisy Tasks Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research

Reference 6

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source=pdf_text observed=2026-08-07T12:45:40.600743Z digest=sha256:0cfa945d43740b3fedea02527ae93c47840a879dcb98fb78b5300b87f7f9fec4

Observation 4ab20e1e-e862-4cfd-9f89-edea607bd9d5 · outbound

This paper cites Leetcode dataset,.

LLM Performance for Code Generation on Noisy Tasks Leetcode dataset,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:40.722941Z digest=sha256:bb828fd657a82f786189ba488f243a7d17a8f87056b04b87a9e61f17c93c42bf

Observation ba28657e-6d91-4f2d-9d5f-655ba170c91b · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

LLM Performance for Code Generation on Noisy Tasks Measuring Mathematical Problem Solving With the MATH Dataset

Reference 8

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source=pdf_text observed=2026-08-07T12:45:40.821937Z digest=sha256:0636403926dc7d7bb4de197fa9d1f32aa5921c2cd97e5e2b7222f2ecce2fd4e7

Observation db0cddfe-a008-45a4-ae18-3d9528a862a4 · outbound

This paper cites Data Contamination Through the Lens of Time.

LLM Performance for Code Generation on Noisy Tasks Data Contamination Through the Lens of Time

Reference 9

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source=pdf_text observed=2026-08-07T12:45:40.967359Z digest=sha256:cbe86f9eae76978c0040972874c293913c7ba16cf9f0f4a5a868494b7544a6cd

Observation dc4387f9-e218-4af0-a674-4f10df6c376e · outbound

This paper cites Dynabench: Rethinking Benchmarking in NLP.

LLM Performance for Code Generation on Noisy Tasks Dynabench: Rethinking Benchmarking in NLP

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:41.067226Z digest=sha256:5eabd2e7b35c22c38a0425b7b6243fafd01435d06c30d9fec3806af25204ddd1

Observation 78c3158d-32db-4027-9bb4-19bb3fdb2eed · outbound

This paper cites Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges.

LLM Performance for Code Generation on Noisy Tasks Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges

Reference 11

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source=pdf_text observed=2026-08-07T12:45:41.204856Z digest=sha256:7d6aeee066f1c2a4ba87c806853e3c894b2b0c5c30672b04b539b527c07c996c

Observation d4b4295c-012c-4306-a64f-9614f6f4ede3 · outbound

This paper cites A Comprehensive Survey of Contamination Detection Methods in Large Language Models.

LLM Performance for Code Generation on Noisy Tasks A Comprehensive Survey of Contamination Detection Methods in Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T12:45:41.334513Z digest=sha256:5fd413cd15190211fe7e30612af1a8f559200af9dbd8c57d779ee7368653d558

Observation 2b258087-4b1d-4172-b964-beb4201d1fb9 · outbound

This paper cites Resilience of Large Language Models for Noisy Instructions.

LLM Performance for Code Generation on Noisy Tasks Resilience of Large Language Models for Noisy Instructions

Reference 13

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source=pdf_text observed=2026-08-07T12:45:41.427522Z digest=sha256:5998f72f5e204a3cafe380d91bcdeb1cafb4ff3d49ceec6f170a03fa1e19f96c

Observation 14ec85f6-726c-4c66-92e6-cf86351984fe · outbound

This paper cites Measuring massive multitask language understanding,.

LLM Performance for Code Generation on Noisy Tasks Measuring massive multitask language understanding,

Reference 14

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source=pdf_text observed=2026-08-07T12:45:41.565070Z digest=sha256:00e5fa33333d3f25377ea7cfa7809021229294ef8bb08e606b4aaf9250604e51

Observation 7ff095dd-c6d6-4c15-9c27-6d4ea8b62f07 · outbound

This paper cites Impact of noise on llm-models performance in abstraction and reasoning corpus (arc) tasks with model temperature considerations,.

LLM Performance for Code Generation on Noisy Tasks Impact of noise on llm-models performance in abstraction and reasoning corpus (arc) tasks with model temperature considerations,

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:41.803492Z digest=sha256:ae634876aa10249190f7ffe740484f92a73cb36a8bb830c84910d041f00fc35c

Observation 9310f242-bb3b-4610-b5d4-0dfc4510c825 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

LLM Performance for Code Generation on Noisy Tasks Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 16

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source=pdf_text observed=2026-08-07T12:45:42.040015Z digest=sha256:04eb5b8383d1ac51a370b9dfae3575c81c71b9bc451c344c1b56fec17f0af4e4

Observation ec7ef6f0-768c-4196-b2de-863cf6d6e03b · outbound

This paper cites Datasets: A Community Library for Natural Language Processing.

LLM Performance for Code Generation on Noisy Tasks Datasets: A Community Library for Natural Language Processing

Reference 17

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source=pdf_text observed=2026-08-07T12:45:42.162519Z digest=sha256:3e0738ceb15fc0930213e361b16e6f9b35b96aa283853651bf0dc005b0feb2e3

Observation 0342647e-a376-43a7-b107-d8ebe46ad4b9 · outbound

This paper cites Leetcode problemset,.

LLM Performance for Code Generation on Noisy Tasks Leetcode problemset,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:42.206375Z digest=sha256:5be21e3d49a60efed7ff1adf3b91ff5f747c42d987e2cc04fc16ea640520e20c

Observation 80b652f8-998e-4fca-b228-c76a1f4664fe · outbound

This paper cites Math augmented dataset,.

LLM Performance for Code Generation on Noisy Tasks Math augmented dataset,

Reference 19

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:42.330482Z digest=sha256:969d50ccd75db650f937fc5432af7bc04829e726e4654e9984c495e229616685

Observation 2e370611-a598-4830-90dc-6f6eeac107cf · outbound

This paper cites Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming.

LLM Performance for Code Generation on Noisy Tasks Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming

Reference 20

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source=pdf_text observed=2026-08-07T12:45:42.479403Z digest=sha256:9c3fa88c9078b95fd0a2ad3148e2f8bd62d285e5a4dae6d01127972089be023a

Observation a89e5806-0e9d-4826-b762-d17d37bb3a16 · outbound

This paper cites Openrouter: Unified api and playground for large lan- guage models,.

LLM Performance for Code Generation on Noisy Tasks Openrouter: Unified api and playground for large lan- guage models,

Reference 21

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:42.626771Z digest=sha256:ce75bd0969ec6908b78122689358ad36a1106c9605c14d8df06978d616f3f684

Observation 0cf8612f-703d-4c01-a6a4-ca824f8a2d19 · outbound

This paper cites Claude 3 model card october ad- dendum,.

LLM Performance for Code Generation on Noisy Tasks Claude 3 model card october ad- dendum,

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:42.776857Z digest=sha256:24cd136d3bb8129469e64ca1810984be29a8f42c75cda8e73dd6cec59baad3b9

Observation 32ba1988-750b-4b99-962c-356c99ce6e07 · outbound

This paper cites Introducing deepseek-v3,.

LLM Performance for Code Generation on Noisy Tasks Introducing deepseek-v3,

Reference 23

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source=pdf_text observed=2026-08-07T12:45:42.920903Z digest=sha256:6dd23617bd50970968a6fcf1c8435ca6b75fb0cee45a5d671aeba4774f211c8d

Observation 26899376-76be-4aef-8469-b302ef4dfce0 · outbound

This paper cites Gemini 2.0 flash,.

LLM Performance for Code Generation on Noisy Tasks Gemini 2.0 flash,

Reference 24

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:43.036742Z digest=sha256:b107f531e04c08392c4789ac22bc709bca861d3233cef8f24de25c3a93a78613

Observation 2d0e6f62-328a-46b6-8b69-7dc52f4de629 · outbound

This paper cites Llama 3.3 70b instruct,.

LLM Performance for Code Generation on Noisy Tasks Llama 3.3 70b instruct,

Reference 25

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source=pdf_text observed=2026-08-07T12:45:43.145514Z digest=sha256:d0d3f62c548b711baa084485d67a7753a8f12946596c4005aad23600595f6036

Observation 2b1fb22a-a6f0-45bf-9af2-07c425797197 · outbound

This paper cites Gpt-4o-mini,.

LLM Performance for Code Generation on Noisy Tasks Gpt-4o-mini,

Reference 26

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:43.267076Z digest=sha256:39baf21856461f4cb00a40cf0247f59b734813d9d08955788d28c26c8db5a977

Observation ec566a92-e435-4bc6-8eb7-23f91e20f791 · outbound

This paper cites Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval.

LLM Performance for Code Generation on Noisy Tasks Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Reference 27

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source=pdf_text observed=2026-08-07T12:45:43.375530Z digest=sha256:cf6f99608479f8b432830803e0b0ca80c8e235b94c4801cced0f3c07a877b327

Observation 5d0b8d27-9cf4-43d6-86a3-e1ebd65050b8 · outbound

This paper cites Keeping an eye on dangerous python modules,.

LLM Performance for Code Generation on Noisy Tasks Keeping an eye on dangerous python modules,

Reference 28

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:43.496835Z digest=sha256:2967c442129797e1f2ff0bc7be3250c226fb55c5a2539a40df42094394698494

Observation eb6c9b61-7f27-429f-8261-7d9ad7f76aa0 · outbound

This paper cites A mathematical theory of communication,.

LLM Performance for Code Generation on Noisy Tasks A mathematical theory of communication,

Reference 29

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source=pdf_text observed=2026-08-07T12:45:43.638653Z digest=sha256:bcae8dbf03767a7513d6ea45dab38c1796083f15d9a56bda53d0f3ec1add52c8

Observation 804d2c58-59c5-429a-b13a-b27f5a184709 · outbound

This paper cites What are bob and alice saying? [mis]communication and intermediation between language and code,.

LLM Performance for Code Generation on Noisy Tasks What are bob and alice saying? [mis]communication and intermediation between language and code,

Reference 30

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raw_fallback, observed 2026-08-07T12:45:45.680712Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:43.766764Z digest=sha256:484afb8a3487d56503008555d163a3f48ca03b54edefe1f061fb49e03b23e2e9

Observation a4ec811e-cc65-4c54-adde-79bac3377ab1 · outbound

This paper cites Zittrain, Intellectual Debt: With Great Power Comes Great Ignorance , ser.

LLM Performance for Code Generation on Noisy Tasks Zittrain, Intellectual Debt: With Great Power Comes Great Ignorance , ser

Reference 31

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raw_fallback, observed 2026-08-07T12:45:45.423296Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:43.894121Z digest=sha256:681297db17b6a6ca3371740e0d85cf9b995bdf76987945e4a1734774e482bf89

Observation fedb689d-251f-472a-87ad-c2897492e599 · outbound

This paper cites The systems engineering approach in times of large language models,.

LLM Performance for Code Generation on Noisy Tasks The systems engineering approach in times of large language models,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:44.110888Z digest=sha256:877ceef5e432d66900433cf08692865201f6527f1f84a11a07be79faaa707c68

Observation db350097-0094-4912-8d0b-2cc473d66803 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LLM Performance for Code Generation on Noisy Tasks Measuring Massive Multitask Language Understanding

Reference 2021

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:41.680009Z digest=sha256:87d73360daf306e5517df74d4a24f45e7d8ccc9be3ee358504499b5e4e15f1e2

Observation 01888682-4466-44e0-9d8f-f0328c7d998f · outbound

This paper cites Impact of Noise on LLM-Models Performance in Abstraction and Reasoning Corpus (ARC) Tasks with Model Temperature Considerations.

LLM Performance for Code Generation on Noisy Tasks Impact of Noise on LLM-Models Performance in Abstraction and Reasoning Corpus (ARC) Tasks with Model Temperature Considerations

Reference 2025

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local_arxiv, observed 2026-08-07T12:45:44.803019Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:45:41.896216Z digest=sha256:4d4c3eab91b9e4e2b247b8c95b55d28cf91424b0fd0b438af0d768b751d864f7

Pith citing papers

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