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Paper Citation Record · LEDGER

ICE-Score: Instructing Large Language Models to Evaluate Code

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2304.14317.

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

pith.paper-citation-record.v1
2304.14317 v2

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 2026-08-12T06:34:41.77262+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:18:31.186206Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T07:32:00.276184Z

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 dfa5f254-36a3-4fb5-bb39-a042b6271761 · inbound

Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering cites this paper.

Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T14:18:31.186206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:18:31.186206Z digest=sha256:df7de4bd7a55dd0ffc7c4b03d8bb75f69c4748f3a4f447f5ec04bece3700dc3f

Observation a931a814-1e50-4419-8454-83330b2aedd9 · inbound

Human-Like Code Quality Evaluation through LLM-based Recursive Semantic Comprehension cites this paper.

Human-Like Code Quality Evaluation through LLM-based Recursive Semantic Comprehension ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-12T05:35:51.200654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:35:51.200654Z digest=sha256:198c8c6bbd991e8f169cf5eeafe9792cba7401f4363557358b4db610099a5234

Observation 475944e1-066a-48d3-8887-8dec6e7ef09f · inbound

Harnessing Large Language Models for Curated Code Reviews cites this paper.

Harnessing Large Language Models for Curated Code Reviews ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T04:51:23.674676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T04:51:23.674676Z digest=sha256:2fe4e31821242e3eb5fd2e5aa1e41a72ea96965ec37aee357f421dcb5b833cd4

Observation bd8878b1-3bcb-4fb3-ba83-47ba142e2d6e · inbound

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code cites this paper.

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T19:34:36.936956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:34:36.936956Z digest=sha256:381b524517c3e8aedc984d707348017afe1a4c1f4b8052b8bf4a5ea545562471

Observation 67c31f2d-c63d-425a-aa85-98d694b3845b · inbound

Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering cites this paper.

Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:32:00.277459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T07:29:03.994957Z digest=sha256:cc20fcfd87e463ae2437e43322ae901d90c563c4467b0491dbe13d0eb31f6bea

Observation 11d3349f-b591-41f4-bd36-afda58905c82 · inbound

Balancing Usefulness and Naturalness: An LLM-based Curation Pipeline for Code Review Comments cites this paper.

Balancing Usefulness and Naturalness: An LLM-based Curation Pipeline for Code Review Comments ICE-Score: Instructing Large Language Models to Evaluate Code

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-13T02:24:25.366633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T02:24:25.366633Z digest=sha256:70513f3a5046a727986046893ee0988c9111146fad46b511df91a3e31db76894