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Paper Citation Record · LEDGER

LLM Performance for Code Generation on Noisy Tasks

As of 16 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 2026-08-16T06:30:59.297886+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=sha256:5c6b7794e28f4c0b3d098f5f42b7ba5373ca67351106538cc775418f2a609e4f

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:b7d23beabcd6bab5dbda42d3da6fc2fec17a7b149d1ba0b6888951be72c55812

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:d02641ec90fa264594a8a2da43694981af770f01a3c198eac08d9271693f4601

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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raw_fallback, observed 2026-08-07T12:45:48.092206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:40.367337Z digest=sha256:22e1331f24b5b3b3c87c5720cb7989492e4387fac6e3541355dfe0bdf68b7b3b

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=sha256:76d385ea55f1f3aa69fa3d088e2bef30b3fe7a384dc29c34003bbbcf1f1fdd40

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:d322f4c59c3f3628ba8ab952d61b2c950c4e950940ec20c2c706973e920dc617

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:40.722941Z digest=sha256:bbe0cba62d1f21f5287286fb765d5e8090ecf2b971adaa47a93e923d7ff52cbf

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=sha256:01df1dbc1ba3c7174b0896f995993bc3580745fd4167f28c1940b4d8a8ad9a2b

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=sha256:9d87058f941aac876c47428322a11be952c12df322a0c77e5ce60d327040a9b2

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:f913f474f22ea54ecb2c5b53889fa2fd1a155ec30748277778c125d6977c4919

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=sha256:863b5538135c29f8f12830a719c737281c6e1bc17a4ff7ea207113eb332fd69f

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=sha256:2f800e92aca4675fe184862bc46ca2f9e889fe9dbc5a1335a5c03c0741031ec5

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:daa8a6c31e4290fb69ef98a746a7e26df0f1e869f779dd3025a938a2d1e9cf09

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:d88eb3b3787fe54acea121f0515ed728c87e65e81dce051599b21a2d85077971

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:41.803492Z digest=sha256:ee6bd256099d1a73b833fddec4c1da0140e73622dfd357a16acf5ca480cae4dd

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:f8c4d87797ed296d7b6658ab80f12063a68f84e04b9dee0529ab878de761371d

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=sha256:2ab8b9226a042c8b26ed6fc516764457df86da25f7b05271118825365b9394a0

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:42.206375Z digest=sha256:4b3c62c9924733c16dd18fa20c7bbef4be7a68e980e9278a9c170262cb3828c1

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:42.330482Z digest=sha256:22810a9865c08a02eabb0b71275a010cc16d00f5077654fc7793baecf23e847c

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=sha256:24d30ebc83d5a92bc82db7495086949fe579322f1f32ea021309cedc30ba25db

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:42.626771Z digest=sha256:3e6afc7dda20d2e88b010a5aca0aee2e69c1bb09ea578647cf0764af839db8e6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:42.776857Z digest=sha256:e00a6dcd47df323450c9aa76aa408bd75ae5b60e92e8191c3b0a19edf0fcfa84

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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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:42.920903Z digest=sha256:92a6bf59f69005e8a3c00d3820107207a6bcc990537f7f01df19149dbd1b038f

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.036742Z digest=sha256:c039847ace92d9db5edc000b4553f90ff8fbfe07867bc5dd515a348726e794f5

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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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.145514Z digest=sha256:72d0576eacfc32fc2629521721e42ed004fe11ba3bdc166c1b2b34e84ce85a5a

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.267076Z digest=sha256:117cd0169f4b44b455a2cce05f966effd7e5c681291b9b261f504c49bd298d68

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:fed440e5f8b040e1127dcb45ea94521337eb8249ac8ea4325ce34288043b7b31

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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raw_fallback, observed 2026-08-07T12:45:45.894471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.496835Z digest=sha256:db8330ba9c5988830629d5476740181e0a98de4f768b36ebd76c3e093e14277b

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-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:45:43.638653Z digest=sha256:c088536e797327e2d7de7cde5f4c7a8417d98cf7c03ebefeb096385f79a5f86a

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.766764Z digest=sha256:134dc3e331538c30749e0ebe7722b496a858939750c5492246d514b09ccb6567

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:43.894121Z digest=sha256:b02c4fd014da21d8fcb69ab286e88f1dab17238f1da86b83597a60b88fde1e5d

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:44.110888Z digest=sha256:4a26c467229543f5f49fba52a161355a7cbc074568d1de8129b602c75c9ea616

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=sha256:0050b88f5fa9cf31e2dda31054a9603961f83c6a889a015a02d66c6036bb6429

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T12:45:41.896216Z digest=sha256:3b487daa1b5f47d9be538e056744fc9cf5b5e5c4dd15f6be4329943debfd6521

Pith citing papers

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