Pith. sign in

Paper Citation Record · LEDGER

An AST-guided LLM Approach for SVRF Code Synthesis

As of 8 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 2026-08-08T06:32:00.761636+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:b5b3b88a71a9c89265c00717db79ebfd716be8574ac5cf36ad0f5f432569a2c0

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:bd8183f8d90884451a6e356b7d1be047814132591e201e9b19aff4d080df0662

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

source=pdf_text observed=2026-08-06T21:24:38.849038Z digest=sha256:8f085fb22f03a8d91c5b9320069ffc6fabb896c48cfad12e45c7956abf7ff722

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

source=pdf_text observed=2026-08-06T21:24:38.902563Z digest=sha256:c4cb222e0aa40ec7b4524fd66bbdb2989f4e64a4d71fdea65d4d9d75b76bb529

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

source=pdf_text observed=2026-08-06T21:24:38.937676Z digest=sha256:65eb30bc90480a3c579f66800a880757dda8ada123aa40df13a6dd6002d8ca8e

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

source=pdf_text observed=2026-08-06T21:24:38.986506Z digest=sha256:573a46c9554229a7c8f9487eb0a2c1e3ccedaadce177316429cb57ece6bfaa6a

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=sha256:31a816043c84f7708b50cfe5e7560ab58f904db17236fe9bf5867ac95c712ce8

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=sha256:391aff6ddb8070b556f9b0277bdded04bc2653ca2c023b7816aad176326ed64a

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:da916197d3eb2efcbf0ebc63e91fcd39d95174723a8bc0880ef4766eac2ea10c

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:ed779e4f09ec680ff0c9d56af765977e6874ed50fc348b966bcf762a71e10fec

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

source=pdf_text observed=2026-08-06T21:24:39.272541Z digest=sha256:ea8429ba5d9c4350c48f61d09f511ba0a924e2b39d81bad70a488be68d425b44

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

source=pdf_text observed=2026-08-06T21:24:39.321383Z digest=sha256:3cab6071db287b286cc67f56f14fe694eb1a3b84e5b0b17f9ae02a06f27eabd0

Pith citing papers

No inbound Pith citation observations are available.