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Paper Citation Record · LEDGER

AceCoder: Utilizing Existing Code to Enhance Code Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2303.17780.

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

pith.paper-citation-record.v1
2303.17780 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T20:37:30.660119Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T02:23:46.148525Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • 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 3b9fbc9e-d50b-4179-88c1-45dc299fe10e · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 152

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:32:17.406683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T13:32:17.177021Z digest=sha256:20c167af58ce7d1dc70525872c5e9be569596e1e780e83767251b42d840da2c2

Observation e43c588f-52be-4ee6-93e1-e84185ca45bb · inbound

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation cites this paper.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-24T02:23:46.151950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T02:19:23.135463Z digest=sha256:f7b6470427aadee0095fd6a24b1621004121c3f4f909c20d82e6c5269df1c3e4

Observation 75cede3e-f5e5-4245-b8f6-23bbd2874dc5 · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 148

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:18:06.769232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T20:18:06.304134Z digest=sha256:fbe1172c5a8aa7c99bc7c014f78621472ddc9bd49c189fbb7e705543e62c1864

Observation bac31ff0-8366-4d55-96e4-c8ee6091dd34 · inbound

Large Language Model-Based Agents for Software Engineering: A Survey cites this paper.

Large Language Model-Based Agents for Software Engineering: A Survey AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-17T12:35:48.531360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T12:35:48.170947Z digest=sha256:aec0436c12fae6dba07c1f3d9adf35e5bab1112e35038881a827a1f0c57ed209

Observation d9b423e1-6c0d-4819-8113-43949de194ff · inbound

Knowledge-Enhanced Program Repair for Data Science Code cites this paper.

Knowledge-Enhanced Program Repair for Data Science Code AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T20:37:30.660119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:37:30.660119Z digest=sha256:b3968665f4a5cc8bdc73eeacb11754fd498ab04a2cbf3f4493bdb490e69992fc

Observation 08e48aee-ec9f-4561-ba4e-aa08194d2d8f · inbound

LOCOFY Large Design Models -- Design to code conversion solution cites this paper.

LOCOFY Large Design Models -- Design to code conversion solution AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T15:20:34.166467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:20:34.166467Z digest=sha256:8763a758e650599307eb0a6cec8dbee20c5bdd228fea2f8661a7a0df3448dc6d

Observation b0378ef8-148e-4e68-9277-ad54c151de77 · inbound

GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion cites this paper.

GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T04:48:35.991772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T04:48:35.991772Z digest=sha256:c2bc6926046d6875f7b6bbe35afeedc94806492e0e438c2cc6d9cb6ef5d96cdc

Observation de631838-cc85-4b89-9e0c-30386df19b5e · inbound

Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study cites this paper.

Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-17T03:38:58.014539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T03:38:40.078229Z digest=sha256:304eec9ff2d081e1e08bbd76b60986cff6e199bd6d7a5b7d5cf539e07747aed8

Observation e4375a95-5543-44ac-a2c4-8d43564f06aa · inbound

Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion cites this paper.

Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-03T06:14:26.234180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:14:26.234180Z digest=sha256:1ee8ae7fd50b09cf843562e105318eb28432f5141ed03ce30a5fd1a5db8b9cd7

Observation aa58d154-05ed-4657-9c42-a53666e9f50f · inbound

RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models cites this paper.

RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T00:57:36.767904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:57:36.767904Z digest=sha256:ddbf38b5548a0605c846cefcf96598d39d3ce247b5e4ccf93cf054c155720ddc