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Paper Citation Record · LEDGER

Analysis on LLMs Performance for Code Summarization

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.17094.

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

pith.paper-citation-record.v1
2412.17094 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:50:08.066577Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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 exact1
  • verified fuzzy17
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74ddc12d-8245-4d9b-b638-8673d2e1532d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Analysis on LLMs Performance for Code Summarization Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=pdf_text observed=2026-08-11T05:50:07.945617Z digest=sha256:fa760629f014dbc070439417912495fa9392e5d69f1759a99a14252aff35d9c0

Observation ea9794dc-ee17-4fb9-9a0a-e9f627bdaee9 · outbound

This paper cites Gpt-4 technical report,(2023),.

Analysis on LLMs Performance for Code Summarization Gpt-4 technical report,(2023),

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.950125Z digest=sha256:aa835de49e85f768316400a182b1653a907dceac01387ee2faa510f1d6642915

Observation 89341afa-b7c1-465f-b5f9-5cb1e797f479 · outbound

This paper cites Few-shot training llms for project-specific code- summarization,.

Analysis on LLMs Performance for Code Summarization Few-shot training llms for project-specific code- summarization,

Reference 3

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source=pdf_text observed=2026-08-11T05:50:07.953528Z digest=sha256:52e71663513e2ccd135fb7abe374128f46669dba627879cb94f9fe18ca725fb6

Observation 668b998e-23c2-414b-9e76-8580d2166916 · outbound

This paper cites Llama3modelcard,.

Analysis on LLMs Performance for Code Summarization Llama3modelcard,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.956953Z digest=sha256:2540f71de1c7f904b0ae12df728e2be6a92f5241d537a87795c35830bc42eb14

Observation b21965ea-baee-41b5-8829-2c1804837f52 · outbound

This paper cites code2seq: Generating Sequences from Structured Representations of Code.

Analysis on LLMs Performance for Code Summarization code2seq: Generating Sequences from Structured Representations of Code

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.960567Z digest=sha256:3df4d058561ed598cee54973f8ea0f020cefa4ce0e45d9229ce1a68793a7b871

Observation 5ac542cd-7c15-475b-bc8e-7d42343eb6a6 · outbound

This paper cites A parallel corpus of Python functions and documentation strings for automated code documentation and code generation.

Analysis on LLMs Performance for Code Summarization A parallel corpus of Python functions and documentation strings for automated code documentation and code generation

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.964494Z digest=sha256:33ae3116fb72f46dbf62f43eaa58b219cf33209e560124936764379b85d6282a

Observation e5909fb8-3b00-40eb-88f9-18d94358b9b8 · outbound

This paper cites GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization.

Analysis on LLMs Performance for Code Summarization GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization

Reference 7

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.968310Z digest=sha256:06cedfafc71ab2c4f020400f3006211b3e53da7f16684b954d71f78424e9b3f2

Observation aa7b2409-d4e3-44d6-a505-4216ef1be13b · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Analysis on LLMs Performance for Code Summarization Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 8

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source=pdf_text observed=2026-08-11T05:50:07.971824Z digest=sha256:7a79de6f9adbf758660bfde5e674e0d330cab58ebffab854d4194d3504904106

Observation db26764d-9730-4773-b0fc-805cf6ee81eb · outbound

This paper cites Structured Neural Summarization.

Analysis on LLMs Performance for Code Summarization Structured Neural Summarization

Reference 9

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source=pdf_text observed=2026-08-11T05:50:07.975359Z digest=sha256:6426946126670467ee1ea7a9d8a73f66c3ca6277ddead62a1b49fccd28030a15

Observation 59551b56-8175-4572-bb80-7e72e0091c7f · outbound

This paper cites Code Structure Guided Transformer for Source Code Summarization.

Analysis on LLMs Performance for Code Summarization Code Structure Guided Transformer for Source Code Summarization

Reference 10

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local_arxiv, observed 2026-08-11T05:50:08.198657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.978730Z digest=sha256:f48c433f73e28dbb7ad4fb512ac79720c2ae2cc46472ef78bfdb99121274364f

Observation ab072b7f-4b42-4b82-b010-d4ca2bea8fad · outbound

This paper cites M2ts: Multi-scale multi-modal approach based on trans- former for source code summarization,.

Analysis on LLMs Performance for Code Summarization M2ts: Multi-scale multi-modal approach based on trans- former for source code summarization,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.981968Z digest=sha256:e65058d1a9dc4e7c13e042df001c80e5b5a40e6c3246c0136632cd22153a1e36

Observation 311a94bf-470e-45c5-b1d3-b022b9fd8b0a · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

Analysis on LLMs Performance for Code Summarization GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:07.985661Z digest=sha256:9781780593f2875aad1fd10c74532aa235210debe42260265a9cf2392076be14

Observation 4e724de9-1390-4c15-b383-8b66371ff842 · outbound

This paper cites Analyzing the performance of large language modelsoncodesummarization,.

Analysis on LLMs Performance for Code Summarization Analyzing the performance of large language modelsoncodesummarization,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.989218Z digest=sha256:3eee7649d0212bd02f99e7df7fc9ca51ed970b492ad9cc17ff04114f707c4b10

Observation 298046f8-5583-4f99-bbac-ab0d5418793b · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

Analysis on LLMs Performance for Code Summarization Measuring Coding Challenge Competence With APPS

Reference 14

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source=pdf_text observed=2026-08-11T05:50:07.992821Z digest=sha256:e87f7067b6c9788fb07d153d4653099e51fcab776711868bd7762088db610a13

Observation 575196ff-49ef-4477-9aa8-4cb43b4f3b58 · outbound

This paper cites Deep code comment generation,.

Analysis on LLMs Performance for Code Summarization Deep code comment generation,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:07.996819Z digest=sha256:77bae0108699a1cd2c0c74fb703d1c25b93d75860cd3402d9dd9b26277e3d952

Observation 3854f393-9d8c-4373-93ea-bffb9ebf5376 · outbound

This paper cites Summarizing source code with transferredapiknowledge,.

Analysis on LLMs Performance for Code Summarization Summarizing source code with transferredapiknowledge,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.000591Z digest=sha256:76eca49b9e08dc4813f719e9b780efccf8e84ed239fb79b9c14fcf83949856a7

Observation 8066b834-0615-45ac-b671-0dfa95a8bce0 · outbound

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

Analysis on LLMs Performance for Code Summarization CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 17

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source=pdf_text observed=2026-08-11T05:50:08.004115Z digest=sha256:8db53a183fca5beef464fa928a7559cad4d6badf4c374ffa5575fdad6a833655

Observation 456e59f9-52a8-44f2-8cbd-7ab34bceec8a · outbound

This paper cites Summarizing source code using a neural attention model,.

Analysis on LLMs Performance for Code Summarization Summarizing source code using a neural attention model,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.008171Z digest=sha256:c5bd54d3735e228826e4431facb6771e7b59324495a1998ebf933b47ebf666c4

Observation 24e04c51-5324-468a-9084-495f21be72a8 · outbound

This paper cites Mistral 7B.

Analysis on LLMs Performance for Code Summarization Mistral 7B

Reference 19

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source=pdf_text observed=2026-08-11T05:50:08.012080Z digest=sha256:61b33ef311bd2efda8861108ddf4efd05df2e232c93bc463b5c4e7559c59f637

Observation 89bd2816-fde2-482e-ad0d-1c551b575645 · outbound

This paper cites Improvedcodesummarization viaagraphneuralnetwork,.

Analysis on LLMs Performance for Code Summarization Improvedcodesummarization viaagraphneuralnetwork,

Reference 20

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raw_fallback, observed 2026-08-11T05:50:08.434028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.016258Z digest=sha256:197003a5badd078baefdb683ba934560c0b71748662181e9476c2b6000292649

Observation 94478b1e-6eea-4368-a33a-850f7afafa03 · outbound

This paper cites A neural model for generating natural languagesummariesofprogramsubroutines,.

Analysis on LLMs Performance for Code Summarization A neural model for generating natural languagesummariesofprogramsubroutines,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.020062Z digest=sha256:94b37b30680989448ea2f9dfdf197fecf6315e849d4965b283322f371916cf2e

Observation 8a295db7-36ff-49ec-912f-5198118c223d · outbound

This paper cites Retrieval-Augmented Generation for Code Summarization via Hybrid GNN.

Analysis on LLMs Performance for Code Summarization Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:08.023574Z digest=sha256:6271e17a410a1d0c94ec13a21b3297ed64d75e5cac3a1ce545b57c7b1dffa709

Observation 777d7a3c-d540-42fb-9f47-1d652e7d22b4 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Analysis on LLMs Performance for Code Summarization CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 23

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source=pdf_text observed=2026-08-11T05:50:08.027100Z digest=sha256:ce6b9034ef9fed24b6217cf029ff31ba55473169f78e1508c2b2bdd47bf04bdd

Observation 5a1c5c1f-4b81-4bb2-a4fe-774e3706eb50 · outbound

This paper cites Codegen:Anopenlargelanguagemodelforcodewithmulti- turnprogramsynthesis,.

Analysis on LLMs Performance for Code Summarization Codegen:Anopenlargelanguagemodelforcodewithmulti- turnprogramsynthesis,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.030623Z digest=sha256:85d314d3ae871eb1c578af73f061ad6ed1560df0e2dbb786219ac1794fe02f7b

Observation a9f93254-5033-4b31-8ab1-65cafbff3ff6 · outbound

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

Analysis on LLMs Performance for Code Summarization Code Llama: Open Foundation Models for Code

Reference 25

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source=pdf_text observed=2026-08-11T05:50:08.034012Z digest=sha256:92adb1576beaf482cf14763a82fb16942cf2318734c607094c154f26fc48dd4e

Observation f775a6d0-7930-40b0-aee9-5f3a8326f524 · outbound

This paper cites Au- tomaticsourcecodesummarizationwithextendedtree-lstm,.

Analysis on LLMs Performance for Code Summarization Au- tomaticsourcecodesummarizationwithextendedtree-lstm,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.037529Z digest=sha256:0c6cab3ed0071d3572d13fe3b112e79ad6a175ea27e5ac3c1362ccbde9f4bfc8

Observation f91aa0c3-7678-46c1-bc64-2776a887b434 · outbound

This paper cites Sequencetosequencelearningwithneu- ralnetworks,.

Analysis on LLMs Performance for Code Summarization Sequencetosequencelearningwithneu- ralnetworks,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.040865Z digest=sha256:8b81b07da895156d1f5b7de60c6d0c7be49da5bd39acd167b109d37a69daf655

Observation d621579c-9b9b-4e3f-9220-a98cbd3f1521 · outbound

This paper cites Ast-trans: Code summarization with efficient tree-structured attention,.

Analysis on LLMs Performance for Code Summarization Ast-trans: Code summarization with efficient tree-structured attention,

Reference 28

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raw_fallback, observed 2026-08-11T05:50:08.377964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.044163Z digest=sha256:8f29728fcaf2052fcf08a66265052ab3b9d32e4613bd71c77e050e4f632e889a

Observation 38c38901-65c5-41e5-b9f6-4d82d908a848 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Analysis on LLMs Performance for Code Summarization Gemini: A Family of Highly Capable Multimodal Models

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:08.048039Z digest=sha256:8623fb77077de965a7e9abfa3fbb7f7b952b195bd47f7cbc7e73f105929c7a8f

Observation 59ed357a-985e-4ce7-a0b4-9dd1e1aac951 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Analysis on LLMs Performance for Code Summarization Gemma: Open Models Based on Gemini Research and Technology

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:50:08.053155Z digest=sha256:8c6220a5b1bca409216884981f34aeef435399a60c03a89d19e4c5d3a3f67e8d

Observation 6e9feecc-daea-4f92-9786-9af8f7924748 · outbound

This paper cites Attentionisallyouneed,.

Analysis on LLMs Performance for Code Summarization Attentionisallyouneed,

Reference 31

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raw_fallback, observed 2026-08-11T05:50:08.366167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.056789Z digest=sha256:2618fb45a5473e2b0fe1955a9802ae0d4b69cdbbe0124dad08d869029f5d4f54

Observation bfd22a0b-e80c-475c-846d-f95a94711c86 · outbound

This paper cites Improving automatic source code summa- rizationviadeepreinforcementlearning,.

Analysis on LLMs Performance for Code Summarization Improving automatic source code summa- rizationviadeepreinforcementlearning,

Reference 32

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raw_fallback, observed 2026-08-11T05:50:08.355134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.060191Z digest=sha256:6b8a4b84490d46f729c2a077b85b3015959a385946f2bce0b4bee092bb848d04

Observation 708e712f-bff0-49e2-a2ac-f5dc8a0596ba · outbound

This paper cites Asurveyofautomaticsourcecodesumma- rization,.

Analysis on LLMs Performance for Code Summarization Asurveyofautomaticsourcecodesumma- rization,

Reference 33

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raw_fallback, observed 2026-08-11T05:50:08.343757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.063353Z digest=sha256:4d09c97465aae479a51b5e29e5b089720b8d0dfab81b75385b6cb6b838e9dd71

Observation a3ea10eb-b5bb-46fd-bed7-3951b950ab1e · outbound

This paper cites Retrieval-basedneuralsource codesummarization,.

Analysis on LLMs Performance for Code Summarization Retrieval-basedneuralsource codesummarization,

Reference 34

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raw_fallback, observed 2026-08-11T05:50:08.333912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T05:50:08.066577Z digest=sha256:a89dcf4cb99c75d4c7ad9a1fe58399e7c0647ed23469000d312221d06d2ce0bb

Pith citing papers

No inbound Pith citation observations are available.