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Paper Citation Record · LEDGER

Planning with Large Language Models for Code Generation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 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 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:48:48.891629Z

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

  • verified exact0
  • 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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:b05e06589cfadb0f9868d2746d77adf046c3e201c2afabae86b6525f3a6938f2

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:bb953315409220eca0cde51f7cc43336ad37da50baffd227b37dae5b205b55e6

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:d7a288f8ab686cb4598335952954cced2b38bf4c8cb400a2536dabc46646c13f

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:dc76086b1ea8002a305c8720dc5c04b2f524f25ccc6c544ced425751a41ba86f

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:c4f5744eee3cbc0986823b45fa6b7464452d226514e52dad435c5a3f670dbbb9

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=sha256:15ce62583a1b4d231e1696a55ab8e3c2010d783b53a3dc206c6979978890e092

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=sha256:0be72827bcd953dd480d657d491bb0c96c3440d1e641647cbeb57b21f5749d68

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=sha256:9a03dd12c0bc84f941bfa041052533bb0e118344e2f302b6b09c0f45a690781c

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=sha256:2327d8b5e415e00a0fc0f097b1b7bd332b13df968c352f212c6138d26a7e6e15

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T06:30:39.858246Z digest=sha256:decfb9efb2398e37a8b9e5db849e2ef23f59d5365775bedaf06a3874b7fe560f

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=sha256:9b92b6b1cb31742951cedc12dcb7e80e5a39e993b54642e7b606a95ca41e7677

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:b0df3f5bf3cec63296fdfca2dc1fe1e53ae6b25c57f23ee7f49145afbb35b8f3

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T16:35:16.056397Z digest=sha256:ba07edb82413ede82245322063b637b3c55ce5344570c0895c170c9c741e42eb

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T08:13:43.804756Z digest=sha256:03fa046823c46b31791b051109c27ebc47223fec97a18df57345726501e57f30

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T04:29:33.858344Z digest=sha256:d8c28055cbf5f8d314bde57b57461b81474c5d01e60140fbc2d159e89c149c9e

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T02:52:55.120013Z digest=sha256:3d1cd1b05b1d1e4964dbc6ede76643a1dc101d9941783239f861741a64bf461d

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-07T16:04:48.394294Z digest=sha256:a17fb5a0da5d035aaa11d760fcd0993460df62539a58166b71b95d67ae50604d

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T23:49:28.318260Z digest=sha256:551c7b4049606fbaf60431411c23b92e19316cb09704be936e3b98e13381b002

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=sha256:79ccdd305f9d6e29ff32bc599fc49266a0b97f62dc0f43c9af5748dd63edffcb