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Paper Citation Record · LEDGER

Planning with Large Language Models for Code Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2303.05510.

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

pith.paper-citation-record.v1
2303.05510 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 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 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:38:20.065620Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T15:27:06.057125Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • 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 88d61122-3e14-43e5-a49f-f00af6986c17 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Planning with Large Language Models for Code Generation

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-10T17:34:43.028255Z

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.

source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:9b377fcc59bc177834a0351a77ab27370dd3a0a99e4b793b9a75ba209adcfc29

Observation 3cf798bd-03a4-4c78-8d64-0685640fcfed · inbound

Active Task Disambiguation with LLMs cites this paper.

Active Task Disambiguation with LLMs Planning with Large Language Models for Code Generation

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-08T22:38:20.065620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:38:20.065620Z digest=sha256:b5dbd2f16d7f66a69d654a2989ab9e63777ad162970bc21e226e1970e15f79fb

Observation 89c57b61-669b-4af0-bece-c4fe23651fac · inbound

ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization cites this paper.

ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization Planning with Large Language Models for Code Generation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T17:48:48.891629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:48:48.891629Z digest=sha256:e95e39720521f8549066f78bf56bceef46b1a30f822ec59bcd3a2e41ccae97cb

Observation 47da1288-72e8-417c-8831-b090dda3ac2a · inbound

From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework cites this paper.

From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework Planning with Large Language Models for Code Generation

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-07T23:48:43.671852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T23:48:43.671852Z digest=sha256:670f29c72a27ca41184c87baff2b6ea72234f382e81387075d121f31b04c8739

Observation 78f0f04c-8b72-4920-9d4d-bdc76f798214 · inbound

First Finish Search: Efficient Test-Time Scaling in Large Language Models cites this paper.

First Finish Search: Efficient Test-Time Scaling in Large Language Models Planning with Large Language Models for Code Generation

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:42.814618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:40:42.814618Z digest=sha256:74473a4958bd97720dfd0d9cc760eb14031bd9074396c798b06eae520af61c35

Observation bf01b3ea-3b6f-49b1-b42a-e7343e402e18 · inbound

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation cites this paper.

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation Planning with Large Language Models for Code Generation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:53.376594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:53.376594Z digest=sha256:6e830c70e01cc19d12f654762b57c7a86b3ba024559f436eb6715524eaa2ba10

Observation 315e0a0a-d7ef-4091-bd05-5f507d35b534 · inbound

Breaking the Myth: Can Small Models Infer Postconditions Too? cites this paper.

Breaking the Myth: Can Small Models Infer Postconditions Too? Planning with Large Language Models for Code Generation

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-06T17:41:43.727541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:41:43.727541Z digest=sha256:85b0e182938b2adc4f102d7fd1fe3debcc863e58df867a88139bdd061206c4fa

Observation bbdf5ee1-a7e9-4875-926d-c0dd4acb0659 · inbound

It's Not That Simple. An Analysis of Simple Test-Time Scaling cites this paper.

It's Not That Simple. An Analysis of Simple Test-Time Scaling Planning with Large Language Models for Code Generation

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:06.934747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:09:06.934747Z digest=sha256:84637dbd9f23df8fede68bfbfec64183b5ff01d386caa1bc29e4b563525d646e

Observation 3290d4ab-23ea-4fea-bacf-ac6cc6ab5f09 · inbound

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? cites this paper.

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? Planning with Large Language Models for Code Generation

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T14:17:34.208822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:17:34.208822Z digest=sha256:d02976e36d5445cb65694cdf6e7830e9766877c69982fc4782f57d1793f2b168

Observation ee31c22d-fae6-4f86-91b1-7438f039009d · inbound

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning cites this paper.

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning Planning with Large Language Models for Code Generation

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T14:29:26.568182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T14:29:26.568182Z digest=sha256:6742095b79a856b49b155f570110f890ba34bb7b897084ef43924a9ab29b8b62

Observation e001f309-f4d6-41ee-8021-b8e119e89a4b · inbound

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling cites this paper.

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling Planning with Large Language Models for Code Generation

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:30:59.583413Z

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.

source=pdf_text observed=2026-05-18T06:30:39.858246Z digest=sha256:a61c42b4a7e9ddd945f35a28d34424161e0db86b0ed3365fc98c2bf276f219f9

Observation 25b8fa40-a5a2-40f6-9391-3e07277ee9da · inbound

Concentration bounds on response-based vector embeddings of black-box generative models cites this paper.

Concentration bounds on response-based vector embeddings of black-box generative models Planning with Large Language Models for Code Generation

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-03T23:05:54.598627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.598627Z digest=sha256:f9975a612d5a2445cd52c616d5388d6d40462ce4b985238793e8d33417c56120

Observation 419d005a-2df9-472e-b2b9-bea050c57dc7 · inbound

LogiDroid: Individual Functional Test Generation via Business Logic Extraction and Adaptation cites this paper.

LogiDroid: Individual Functional Test Generation via Business Logic Extraction and Adaptation Planning with Large Language Models for Code Generation

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-02T20:07:14.885883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:07:14.885883Z digest=sha256:3c318238d5c00c5fba665b4b383c1a1b214eb590fd4b7df0d7aa56f30a9e7da4

Observation ee7fbfed-afad-44b8-9522-ea5202a331fc · inbound

AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search cites this paper.

AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search Planning with Large Language Models for Code Generation

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:40:57.536889Z

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.

source=arxiv_source observed=2026-05-10T16:35:16.056397Z digest=sha256:f1ffdf94f85a5a564a56bf01b972e79dc53827613f92f99a0e249c8a5f60150f

Observation 4e6da0e5-8c2b-4cff-b4aa-a2e04cbe7176 · inbound

Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation cites this paper.

Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation Planning with Large Language Models for Code Generation

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-10T08:17:37.635236Z

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.

source=pdf_text observed=2026-05-10T08:13:43.804756Z digest=sha256:7c4fc3b60850fa412bc3f1afe5ec40ac6df8681371644c052a38f55bf351410a

Observation e5906daa-29c8-4d12-be27-690c7ba9c88f · inbound

Gradient-Based Program Synthesis with Neurally Interpreted Languages cites this paper.

Gradient-Based Program Synthesis with Neurally Interpreted Languages Planning with Large Language Models for Code Generation

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:08.276528Z

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.

source=arxiv_source observed=2026-05-10T04:29:33.858344Z digest=sha256:3a083cf75ea97c8f90649f3e5ec02fe0fe8569ada21d30fc99b2c1a31481ca22

Observation 89eb1cb7-b2ea-4682-beac-454cdb4b8f23 · inbound

Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions cites this paper.

Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions Planning with Large Language Models for Code Generation

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:21:53.020009Z

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.

source=pdf_text observed=2026-05-08T02:52:55.120013Z digest=sha256:9e07c03d626797d6329330605d4fc571c9647d4ef8851b2d2c71bc52c98ef9ce

Observation 60b85cac-c470-42a2-8d5b-9fe2c1b20dac · inbound

POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference cites this paper.

POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference Planning with Large Language Models for Code Generation

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:56:12.225239Z

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.

source=arxiv_source observed=2026-05-07T16:04:48.394294Z digest=sha256:393ff504afc0081cbf2c4abdf19774db8151401613a5a94954290342bef42230

Observation a246ac53-96b3-4705-9a7c-09e3a5f28f6c · inbound

Beyond Greedy Chunking: SLO-Aware Sliding-Window Scheduling for LLM Inference cites this paper.

Beyond Greedy Chunking: SLO-Aware Sliding-Window Scheduling for LLM Inference Planning with Large Language Models for Code Generation

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T15:27:06.058795Z

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.

source=pdf_text observed=2026-06-27T23:49:28.318260Z digest=sha256:50107fb20b96a18639a0c4a0be682461cbbd93fce98c7eafd5912f40db4e1580

Observation 34c9818c-a02b-4a85-975d-a0df969a136d · inbound

Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer cites this paper.

Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer Planning with Large Language Models for Code Generation

Reference 38

Resolution
malformed identifier
no resolver link, observed 2026-08-05T18:51:31.240272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:51:31.240272Z digest=sha256:226f2d885d7175cec425c1ac7dba5b1fa1aceded5e86b92b82683dd55a49ad75