Paper Citation Record · LEDGER

Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2503.01245.

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

pith.paper-citation-record.v1
2503.01245 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:15:19.945517Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8660b249-d3d7-486d-b053-1a182dd0df88 · inbound

Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation cites this paper.

Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:07:18.237810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cd6f0e4b-81fb-4e2f-8b48-9e9dc82e7ee6 · inbound

AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation cites this paper.

AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:22:14.511789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bc07677-5ab0-45fd-9694-e0e38318fc2c · inbound

Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT cites this paper.

Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:06.379063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15fb8641-0556-4630-b9e3-bee30b9c21b7 · 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 for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:07:09.143823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98e98fc0-7756-49d1-a249-36c0ef3a0af8 · inbound

Testing chatbots on the creation of encoders for audio conditioned image generation cites this paper.

Testing chatbots on the creation of encoders for audio conditioned image generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T21:25:26.837379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae063ee-7e79-4203-9998-7b2d8b2f0c34 · inbound

MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models cites this paper.

MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:42:38.767989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f9e3b59-996a-42bf-b2cc-e67a11c75e74 · inbound

MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation cites this paper.

MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.201261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57ba12a8-fc3b-4695-8800-0b9afeaed53c · inbound

Can Vibe Coding Beat Graduate CS Students? An LLM vs. Human Coding Tournament on Market-driven Strategic Planning cites this paper.

Can Vibe Coding Beat Graduate CS Students? An LLM vs. Human Coding Tournament on Market-driven Strategic Planning Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T20:17:18.361301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a06da63f-eb4f-4df1-bf90-3074da42683c · inbound

A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems cites this paper.

A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:18:36.813793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30385141-3ff9-4dae-a03e-361e0f599478 · inbound

Token-Level LLM Collaboration via FusionRoute cites this paper.

Token-Level LLM Collaboration via FusionRoute Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:26:31.525401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0848568-4644-44a1-bdb1-4ca36f0ec90a · inbound

RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation cites this paper.

RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T06:09:51.070684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a71fb0c6-f480-48d3-ab4c-48757d6def98 · inbound

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review cites this paper.

Sustainable Code Generation Using Large Language Models: A Systematic Literature Review Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.561595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af9f173e-c753-49c1-a234-f6f66caf101d · inbound

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review cites this paper.

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:56:33.876773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e549f3ee-e5c5-4eba-affd-a13728af17aa · inbound

SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms cites this paper.

SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:46:24.700682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 40be6dc2-b796-4cc1-a673-11a921ff2ded · inbound

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code cites this paper.

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:11.060336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e19e02b-9062-4d9d-b4a8-3711875d4246 · inbound

VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation cites this paper.

VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:01:32.391146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11965997-c9a8-4885-9b46-79e474faf80c · inbound

ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization cites this paper.

ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:59:19.319070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation def247ec-7aef-449d-9410-14c778a72f44 · inbound

ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization cites this paper.

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:14:47.154037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3383f39-c6b5-4fac-ae05-2dfdec72a9e9 · inbound

Enhancing Reliability in LLM-Based Secure Code Generation cites this paper.

Enhancing Reliability in LLM-Based Secure Code Generation Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:14:46.101325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c5036fc-943a-429f-bc5b-abc09efa9d23 · inbound

LLM-based Mockless Unit Test Generation for Java cites this paper.

LLM-based Mockless Unit Test Generation for Java Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:33:39.615944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a9dcdb1b-e440-4144-8c10-08a01656183d · inbound

Exp2VLA: Enabling Vision-Language-Action for Drone Navigation from Expert Demonstrations cites this paper.

Exp2VLA: Enabling Vision-Language-Action for Drone Navigation from Expert Demonstrations Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T04:33:48.376442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4545f6f-8ad6-4bba-8aae-514fa4f7641a · inbound

TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development cites this paper.

TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T14:04:54.404735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b6c7786-0127-4870-a0c2-09cd59c98928 · inbound

Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies cites this paper.

Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T07:58:46.907203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6d190f1-319f-4c69-bca4-2f4488506c15 · inbound

Characterizing the Quality Profile of AI-Generated C++ in Production cites this paper.

Characterizing the Quality Profile of AI-Generated C++ in Production Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T04:15:19.945517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.