Paper Citation Record · LEDGER

Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2503.17502.

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

pith.paper-citation-record.v1
2503.17502 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:07:09.213015Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T16:05:06.182597Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b9d02ee7-03ad-4f43-8a17-d26151c8789d · inbound

SafeTrans: LLM-assisted Transpilation from C to Rust cites this paper.

SafeTrans: LLM-assisted Transpilation from C to Rust Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:14:55.609300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb3b26c6-35a9-4526-9515-18110b1a5b67 · inbound

A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards cites this paper.

A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:46:37.064416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d39f1e12-fb07-426f-8f90-128b74e92af2 · inbound

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps cites this paper.

Position Paper: Programming Language Techniques for Bridging LLM Code Generation Semantic Gaps Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:09.213015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46f03d8b-04c4-462e-aa55-ab5049e5b5fa · inbound

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? cites this paper.

Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior? Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T22:04:09.257591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08382ccf-5a00-4a73-94dc-df87a452893e · inbound

LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering cites this paper.

LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:05:48.323397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb1d5183-9eb1-406b-a953-5639852be598 · inbound

ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation cites this paper.

ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:46:05.168685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a288544-a93c-4cbe-8d97-90200e5819c0 · inbound

Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing cites this paper.

Cascaded Code Editing: Large-Small Model Collaboration for Effective and Efficient Code Editing Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:56:05.632992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4b627c54-6d25-43f2-b74c-4594fbfa245b · inbound

PrismaDV: Automated Task-Aware Data Unit Test Generation cites this paper.

PrismaDV: Automated Task-Aware Data Unit Test Generation Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:49:16.129741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4dd254d-12c4-4d60-aa98-9e1ad014973b · inbound

LLM-Based Code Documentation Generation and Multi-Judge Evaluation cites this paper.

LLM-Based Code Documentation Generation and Multi-Judge Evaluation Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:55:45.131961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aae1eaaa-0b4a-43bf-ae5a-9f29b00f4b10 · inbound

Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries cites this paper.

Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-08T16:05:06.183843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.