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Paper Citation Record · LEDGER

InstructCoder: Instruction Tuning Large Language Models for Code Editing

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.20329.

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

pith.paper-citation-record.v1
2310.20329 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:16:05.068700Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f40e74c5-baea-4cb1-abf3-d755ed8d3e05 · inbound

Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline cites this paper.

Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T23:21:02.100469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T23:21:02.100469Z digest=sha256:a4f2e6d98f0da703bf295b0c3593da2ba7532eab83d45cc3131739560ba214bb

Observation 366d38e8-bb68-45ea-957e-120c546776ba · inbound

Automatic Qiskit Code Refactoring Using Large Language Models cites this paper.

Automatic Qiskit Code Refactoring Using Large Language Models InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:50.160228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:50.160228Z digest=sha256:d1436674ce73a40d814004e3ff6e3c7177fa156d1c291e5950677eab479425ca

Observation fb2ecc9d-0c25-4357-a454-853afeebe869 · inbound

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models cites this paper.

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T17:56:21.349476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:56:21.349476Z digest=sha256:22092b7d0373a63fec19e40553e744fb59cb461fc1e3676efae760a0492ac506

Observation dd8f8ad7-16e5-4d42-ad43-6929f0f3cc63 · inbound

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation cites this paper.

WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-15T17:16:05.068700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:16:05.068700Z digest=sha256:f941eeeca07926dd9582a2599f4997f8ef7e2b677efccb94e0034a89b8e68cb9

Observation 981518e4-9e67-49da-8c6d-52324696a37f · inbound

Qiskit Code Migration with LLMs cites this paper.

Qiskit Code Migration with LLMs InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-06-26T16:29:35.592672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-26T16:24:25.357338Z digest=sha256:7c93d1e4d787784687ae7b547fb2f59b6ff63300623032477bffcf0cf9119418

Observation 15ae6f95-4a19-4628-bfff-d793b0f75419 · inbound

Quantize with Confidence? An Empirical Study of Quantization for Code Generation cites this paper.

Quantize with Confidence? An Empirical Study of Quantization for Code Generation InstructCoder: Instruction Tuning Large Language Models for Code Editing

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-02T03:32:56.669208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:32:56.669208Z digest=sha256:cc1ae79c74626af3533ba1ff8b0a1328f7fa723d4ebf031a700d9b94d84a8d52