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Paper Citation Record · LEDGER

Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2302.04662.

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

pith.paper-citation-record.v1
2302.04662 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:24:39.465264Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:49:41.859448Z

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 59074329-bcf2-4fbc-9847-ef9d30c5cc69 · inbound

How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? cites this paper.

How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T22:05:21.377877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.377877Z digest=sha256:937ca3fe59e167ca80fe40f0b04e1088014cb2e8c0c3e367d7ad99a62d9becc5

Observation 6b8465ab-0820-443e-a187-4a978fcec53d · inbound

From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation cites this paper.

From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-15T23:24:39.465264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:24:39.465264Z digest=sha256:dce2a0a5133da8ddb44b107d16af88ded389b7360a1d852bf1574f85d5663e71

Observation 739ed503-6686-4888-aa18-df11e48b623d · inbound

LLM Contribution Summarization in Software Projects cites this paper.

LLM Contribution Summarization in Software Projects Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T14:44:30.449092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:30.449092Z digest=sha256:f998dfaaf9cb5739d3699c1a556ea2803366400a9a57d2c59c7f5899c7affcc4

Observation c20f1547-b53e-4067-b455-d9a1003d0114 · inbound

Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education cites this paper.

Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:47:12.277465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:47:12.277465Z digest=sha256:f7f8aeaedbb4b7fbfa904f9e5cdae0d354c9e79a953c7be1f3b6c803433c531c

Observation 7b90f0f1-84be-4808-9012-aebc429bbe68 · inbound

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools cites this paper.

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:43.236196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:43.236196Z digest=sha256:b6f5f69c236391a9557fdf3e72b22749da710bdc110da30efae0ae327a9d80c4

Observation 9511c82d-5573-46e5-9537-58ada5d060b4 · inbound

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs cites this paper.

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:51:42.740694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-09T19:09:07.557773Z digest=sha256:804a680ed59a67d39df48ab710a06217b8b1d55584bf80ebb60860231683b9bd

Observation 7ea8e53b-e22f-43b2-9e6a-1ef99b948df5 · inbound

A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming cites this paper.

A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:57:28.640259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T17:39:52.992106Z digest=sha256:ffa93a8e1f2210e9bdad05d84d1a1b7945b3fa2c17a1aa2cc82471ba4be184e2

Observation cb66279a-9fba-4a45-bd5c-3389a24d55e3 · inbound

Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior cites this paper.

Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:49:41.861208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-26T10:59:41.345239Z digest=sha256:1ed162e8c8d4ad3498f0a3ecd6d7b0e3541a328b336548f575d522a2fd287df0