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Paper Citation Record · LEDGER

An AST-guided LLM Approach for SVRF Code Synthesis

As of 19 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2507.00352.

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

pith.paper-citation-record.v1
2507.00352 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:24:39.321383Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

12 of 12 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84cb977a-a65e-4990-ac05-32650afa6f10 · outbound

This paper cites Language Models are Few-Shot Learners.

An AST-guided LLM Approach for SVRF Code Synthesis Language Models are Few-Shot Learners

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:38.744273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:38.744273Z digest=sha256:8bd348261f4cb13b16241ed343e02dd58ac85f58c11164df3ccfaf40d8e6ed14

Observation 5bd897d8-73bb-429f-ab20-c048a8d577ec · outbound

This paper cites A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends.

An AST-guided LLM Approach for SVRF Code Synthesis A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:38.802024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:38.802024Z digest=sha256:8a71f9acd690f2ad5d5774f05cb58c223b3e2892a052cf8d52ccabf7d3f769f2

Observation 634644df-97d2-422d-952d-ccc5de3cdea9 · outbound

This paper cites Parr, The Definitive ANTLR 4 Reference.

An AST-guided LLM Approach for SVRF Code Synthesis Parr, The Definitive ANTLR 4 Reference

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:24:40.214809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:38.849038Z digest=sha256:9615d7b891cf0c4802c25ba2c0c5cd7ed9b93065daf007dedf12953fe5db89f1

Observation c45435dd-1c24-4270-8607-edd0934f0034 · outbound

This paper cites an unresolved cited work.

An AST-guided LLM Approach for SVRF Code Synthesis Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-06T21:24:40.058608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:38.902563Z digest=sha256:24e305cdbce8f7851c50410e7b918af662f4142a503f479049b3cacf7a32f905

Observation 1aa92d61-b40d-41ec-8741-011fb597b07b · outbound

This paper cites Ast-based program transformation for enhanced program understanding,.

An AST-guided LLM Approach for SVRF Code Synthesis Ast-based program transformation for enhanced program understanding,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:24:39.913327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:38.937676Z digest=sha256:7eb4c2315c4b012af1254e72580c1c1bdd2d0e0eac1b8a0a052f0cb38c5e6ea7

Observation fa1dca33-f08d-49d7-98b8-d85bac3ee2ae · outbound

This paper cites Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,.

An AST-guided LLM Approach for SVRF Code Synthesis Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:24:39.793660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:38.986506Z digest=sha256:4f4a94e001fc023271876fc6bad282715a772cf88dae419c878e604c7bc447e4

Observation 7b3ba883-1415-409c-948f-4b8047a010f4 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

An AST-guided LLM Approach for SVRF Code Synthesis Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:39.070465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:39.070465Z digest=sha256:bb18311b12a5b811d0b6b4854437ac7861b87726a4fed97b65ad54ee5b3939cd

Observation 6ad4982e-e8dc-49e7-9788-5025af5dca83 · outbound

This paper cites Attention is all you need,.

An AST-guided LLM Approach for SVRF Code Synthesis Attention is all you need,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:39.123598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:39.123598Z digest=sha256:78ea471a8f3c3ec4a8d2d7cfd8455559114df792f9ef630ab12daea119498708

Observation 736bc56d-e474-4b81-8519-6dbe4a705cc6 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

An AST-guided LLM Approach for SVRF Code Synthesis Scaling Instruction-Finetuned Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:39.183575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:39.183575Z digest=sha256:53e36419f0e4537c31e742197a4c125bf02b2b7e14134538945adfe42a678821

Observation ee91b3ba-aa86-4308-927c-97b5ee434a79 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

An AST-guided LLM Approach for SVRF Code Synthesis Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T21:24:39.223856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:24:39.223856Z digest=sha256:52254b530cdadfd481ee26ed704993a9187412b2263960e01c78ace2a7c8ce79

Observation 4385dbf5-6bf6-4e2f-bf9e-c6db32ae09cd · outbound

This paper cites Rag: A semi-supervised pattern-based learning approach to adaptive code generation,.

An AST-guided LLM Approach for SVRF Code Synthesis Rag: A semi-supervised pattern-based learning approach to adaptive code generation,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:24:39.643269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:39.272541Z digest=sha256:fa8ae8c827671d01278ad735cceecb7cc16a0a55e277800016259b1de44a4d29

Observation c6da42cd-dae2-4119-9724-96e2fae3ba91 · outbound

This paper cites Workflow-based software development: Models, methods, and tools,.

An AST-guided LLM Approach for SVRF Code Synthesis Workflow-based software development: Models, methods, and tools,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:24:39.539602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T21:24:39.321383Z digest=sha256:7ad0d86fe54185bffd839402478dd5fc6c315964b8d3f7297e5618efce3d166d

Pith citing papers

No inbound Pith citation observations are available.