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Paper Citation Record · LEDGER

Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2309.17272.

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

pith.paper-citation-record.v1
2309.17272 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 2026-08-21T06:32:19.484+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:52:04.880765Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:59:19.035398Z

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 520302b2-edcc-4b9b-92c4-d8379cd4d554 · inbound

Political-LLM: Large Language Models in Political Science cites this paper.

Political-LLM: Large Language Models in Political Science Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 250

Resolution
unresolved
no resolver link, observed 2026-08-11T19:52:04.880765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:52:04.880765Z digest=sha256:87c3ac539e2e4537e31fe5cf76d4c3b459bcfd8c6e59473b4dfd4f30f27b3f91

Observation 3c944b78-1647-4ff2-bd2f-9e58d71e06ad · inbound

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution cites this paper.

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T21:25:58.916728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:25:58.916728Z digest=sha256:af6af2ae47267584aa5fb462fcdbfcabd94a7794c5b8ca874841607d77b228df

Observation 05b2843a-be17-4e07-a091-5aa9935f771a · inbound

HackerRank-ASTRA: Evaluating Correctness & Consistency of Large Language Models on cross-domain multi-file project problems cites this paper.

HackerRank-ASTRA: Evaluating Correctness & Consistency of Large Language Models on cross-domain multi-file project problems Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-09T19:46:23.615149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:46:23.615149Z digest=sha256:538c816ea2ae479c8395bdaaa0f97b44f92e1a710860cfbef9b270944c74a608

Observation 6861a483-2a64-4ca9-8b22-1281f01a01ca · inbound

rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset cites this paper.

rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T13:40:17.057331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:40:17.057331Z digest=sha256:8a483d02b3edffec2f294dcb9f2899d9a865240b995f1439fa5d3ca1b682ee7b

Observation f1d29f0e-ab03-40c0-b6b3-1a21a1efbd18 · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 290

Resolution
unresolved
no resolver link, observed 2026-08-06T14:13:07.317950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:13:07.317950Z digest=sha256:76e57becc49ab98942fc04991c555267bb1c6d34aaa2ece2ff77b9ac3bb5103f

Observation 7f2695f9-c611-406a-9a57-0ee2880d27d3 · inbound

Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models cites this paper.

Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:00:57.269361Z

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=pdf_text observed=2026-05-18T05:59:00.400429Z digest=sha256:d84d888feee66bc6fccc42976c78e003cca86148c6d815d325589482614a0613

Observation 4f5d8842-4ad4-4c7b-85cf-7f4441bd339f · inbound

In Line with Context: Repository-Level Code Generation via Context Inlining cites this paper.

In Line with Context: Repository-Level Code Generation via Context Inlining Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-16T18:08:12.838343Z

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=pdf_text observed=2026-05-16T18:04:56.915339Z digest=sha256:0195309ec7795d4c219396c71fc619a359887d01ba98760dab90797a1d863cc6

Observation 9d5a022c-da39-4951-80be-8b62d8ea868a · inbound

Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning cites this paper.

Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-10T05:41:01.887041Z

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=2026-05-10T05:40:25.414166Z digest=sha256:81be64f66f407dbe14781384008f61167affc05f60c9be8f972d3f61e0ccc24a

Observation 11e43c05-2c12-4c2d-b5ec-5463bc01f560 · inbound

XSearch: Explainable Code Search via Concept-to-Code Alignment cites this paper.

XSearch: Explainable Code Search via Concept-to-Code Alignment Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-20T16:33:34.157606Z

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=pdf_text observed=2026-05-20T16:31:27.989614Z digest=sha256:404c19c2ff3e9ee5120d29eece051b0c0504401b9a544340e8b16653890dfc30

Observation e3ad7174-397c-4a40-8da4-6d134e6ce91f · inbound

XSearch: Explainable Code Search via Concept-to-Code Alignment cites this paper.

XSearch: Explainable Code Search via Concept-to-Code Alignment Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:59:19.037933Z

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=pdf_text observed=2026-07-04T00:55:50.772103Z digest=sha256:904e5051d81803b8c201685efdbfdef559b2e5bb01f4a6ec507af77f0f90d583