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Paper Citation Record · LEDGER

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2505.10594.

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

pith.paper-citation-record.v1
2505.10594 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:22:19.152777Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T17:37:51.790000Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:21:10.768449Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f3767cd-7e28-49cb-9d27-0ae7d4320b8f · outbound

This paper cites DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.006242Z digest=sha256:3682d97edc8c72863e74ff24f5f10e9350e94802181076325f52d76d0938e995

Observation 344c348d-7791-4ddd-883d-324c3767f1c3 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 3

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no resolver link, observed 2026-08-15T21:22:19.010922Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.010922Z digest=sha256:0f8f2b528adcb96cd94b21294156b199482e226f83b2d8f6be5737fc57d09506

Observation 2ce294f3-647c-4e8f-9d44-6aae3aff4aa3 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.015555Z digest=sha256:a0a44b2f965329c07e1f490a845eda6fdced6838588a5d4f778271ea03f42813

Observation 0160462c-2e07-4431-9a9d-735280441106 · outbound

This paper cites AlphaMath Almost Zero: Process Supervision without Process.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation AlphaMath Almost Zero: Process Supervision without Process

Reference 5

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no resolver link, observed 2026-08-15T21:22:19.020587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.020587Z digest=sha256:441ebdd89b0b8f9d4e7b12f47fc7ade21eb216e00281f6b5bc92e39285f309c6

Observation 1e219a06-2a95-42f5-a6fe-d0d55e9030d0 · outbound

This paper cites LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.025191Z digest=sha256:615036daa2389a5f1b2374411c446a7ec88ddfb9236b73d48becbe604a98bdf8

Observation 437b3c6f-89cd-45ab-a0dc-8a5c7fca21eb · outbound

This paper cites WizardCoder: Empowering Code Large Language Models with Evol-Instruct.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Reference 7

Resolution
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no resolver link, observed 2026-08-15T21:22:19.030180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.030180Z digest=sha256:008a959bacce2d63f2667480299daca185768d950c2dad8131a5d451cad6c425

Observation aa550ed7-92f5-4c22-8f44-50bed3ee3925 · outbound

This paper cites WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning

Reference 8

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no resolver link, observed 2026-08-15T21:22:19.034292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.034292Z digest=sha256:74b8c29f6d8ce31a2860639935d3710f3c303f5b1eaad9a9eae05bee6d143f5f

Observation 4a896f87-f4d6-48c1-8b0f-5ecb02873cd8 · outbound

This paper cites Magicoder: Empowering Code Generation with OSS-Instruct.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Magicoder: Empowering Code Generation with OSS-Instruct

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.038663Z digest=sha256:c26c94f4c2693f73f2c5522fdf7b13537b0d40320824c1272b99ef4ceb71f654

Observation 44bd3ffe-f9ed-4004-ad79-63854f7f6591 · outbound

This paper cites StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

Reference 10

Resolution
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no resolver link, observed 2026-08-15T21:22:19.042890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.042890Z digest=sha256:6ad27fefd7547668d82d6408f527213b8b3cd0f68f9e40affa7bb7c767f73c6b

Observation 5643fa06-d819-4d27-b1a1-861b482e74a8 · outbound

This paper cites Execution-based Code Generation using Deep Reinforcement Learning.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Execution-based Code Generation using Deep Reinforcement Learning

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.047158Z digest=sha256:04ef0c22dc23fce3b30294792ddb95b857aa87c43aa63923c7cb6751809a5d7e

Observation c5bb4075-ad1b-40fd-b894-284cdbc164f6 · outbound

This paper cites RLTF: Reinforcement Learning from Unit Test Feedback.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation RLTF: Reinforcement Learning from Unit Test Feedback

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.051890Z digest=sha256:c353bbb290023a29ff9587000ca4f17ed0cc6efb6667e3665490098a78851b4f

Observation ce7385d9-2222-4810-9705-ac8a8d97df30 · outbound

This paper cites Qwen2.5-Coder Technical Report.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Qwen2.5-Coder Technical Report

Reference 13

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no resolver link, observed 2026-08-15T21:22:19.058568Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.058568Z digest=sha256:63e391bb203a55532679447cb2a9b1cec32602c338e1b1ef59af8c05baf4c9c6

Observation 51f43649-e44e-4600-bad7-db1d990ba2f3 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.063467Z digest=sha256:ac2162acb837eb1409abbb54e67bbc2bf5da832c4a8b0f7158db7cc7c057c498

Observation 49f79392-dd48-468a-a2a1-f6db8d99afdd · outbound

This paper cites Code Llama: Open Foundation Models for Code.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Code Llama: Open Foundation Models for Code

Reference 15

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source=pdf_text observed=2026-08-15T21:22:19.068428Z digest=sha256:4db25b78cb84eab3a85017e67b04c03f60cad71702b8624c4e5d6a41ce6b6d52

Observation 119594e4-b6b7-493c-a32f-87745aeb9f8e · outbound

This paper cites The llama 3 herd of models,.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation The llama 3 herd of models,

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.073677Z digest=sha256:84f05992fabe424fd5e0605d7abadaae4b486cb93de1a77305c05758210326aa

Observation af981f6a-d750-4d4b-b36c-f02e582510f8 · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation StarCoder 2 and The Stack v2: The Next Generation

Reference 17

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no resolver link, observed 2026-08-15T21:22:19.084505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.084505Z digest=sha256:38b9192cb68e3897df2f54625f495e640585f6c42b7eade757721e2274700259

Observation cb0f0e8f-8a17-40f3-8e18-fc2d25eadc06 · outbound

This paper cites Compilable Neural Code Generation with Compiler Feedback.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Compilable Neural Code Generation with Compiler Feedback

Reference 18

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no resolver link, observed 2026-08-15T21:22:19.089740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.089740Z digest=sha256:f9417c0fe1e0f4b3ee65386db8d8edc54d01318139abc34af149fbbc4aceff85

Observation 69dae1c7-ab8c-4e3f-80ad-9546c12b0e14 · outbound

This paper cites CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 19

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no resolver link, observed 2026-08-15T21:22:19.094558Z

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source=pdf_text observed=2026-08-15T21:22:19.094558Z digest=sha256:1b36532f47b8afef74b5f639585310a899150e59c9f93ae8db34f3e170f6408d

Observation 3bedd0d8-c3b0-47e7-9bdc-1e3bdded469b · outbound

This paper cites CodeDPO: Aligning Code Models with Self Generated and Verified Source Code.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

Reference 20

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source=pdf_text observed=2026-08-15T21:22:19.100005Z digest=sha256:473189bb99b06daa6294bcab5ae3f0915a200e6aa59ca6a7ded92d98cce8cfb7

Observation bc6f4f77-45ab-4ab9-a7fd-029aa5c20698 · outbound

This paper cites Learning to reason with large language models.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Learning to reason with large language models

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:22:19.650894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:22:19.104899Z digest=sha256:5c5880e35f74da486f98a733bab630821266deb70a1be66b9620880553d4beac

Observation ad362d39-9c82-4b3c-b727-12467efd3026 · outbound

This paper cites Qwq-32b-preview.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Qwq-32b-preview

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:22:19.635440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:22:19.109445Z digest=sha256:325258f16cc16458a280a9458624624e8bc91ca81f516df7f695ca2fd2239924

Observation 05764619-47b7-4e38-9e62-f893be94460c · outbound

This paper cites Process Supervision-Guided Policy Optimization for Code Generation.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Process Supervision-Guided Policy Optimization for Code Generation

Reference 23

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source=pdf_text observed=2026-08-15T21:22:19.113985Z digest=sha256:fb2411ab5819a94de421131d37c1896074239bcf604f168c377d9c49c2b3416e

Observation a0a6640d-1baa-48c2-9be6-48915f57311c · outbound

This paper cites o1-Coder: an o1 Replication for Coding.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation o1-Coder: an o1 Replication for Coding

Reference 24

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source=pdf_text observed=2026-08-15T21:22:19.119076Z digest=sha256:8885540e93d76c13dd40dca6664ab1080eaf7e031d01d78df21debc2ebf21804

Observation 59467b44-aa29-435c-bded-98d44f225b1f · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 25

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no resolver link, observed 2026-08-15T21:22:19.124156Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.124156Z digest=sha256:a05f827ed0bb4a74a2cb17572981da61399bbdc496f272f7c0071af22e0c49bf

Observation ac7b7bf2-aa52-44e2-a622-8c2760e801fc · outbound

This paper cites Step-dpo: Step-wise preference optimization for long-chain reasoning of llms,.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Step-dpo: Step-wise preference optimization for long-chain reasoning of llms,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:22:19.619608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:22:19.129667Z digest=sha256:0c124a7499455f34f37e18a733f4a48c63f378f171520882b465b0a901b0964b

Observation ef2701fa-8b87-405e-9596-b2a6a2e94294 · outbound

This paper cites DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

Reference 27

Resolution
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no resolver link, observed 2026-08-15T21:22:19.139788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.139788Z digest=sha256:ba548315e43d8c060aeecd96ef381376bcb9f9f14bc9395f37f3755da3027960

Observation 760fa94c-8fe6-4107-ba6b-d26cc0667127 · outbound

This paper cites Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Reference 28

Resolution
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no resolver link, observed 2026-08-15T21:22:19.134458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.134458Z digest=sha256:2afe81d5a054f054773c14bbd339d3868b33e9de93e03d98b7547f77b3456a9c

Observation e25c6fda-9932-4fa8-a1d8-14274e922e93 · outbound

This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T21:22:19.144150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.144150Z digest=sha256:65704e6dfbba35114065b9d0bf93ec177cba9799a83ee7ed2b2fe1178bf50a50

Observation c8b1647e-4183-4857-aeb0-3793662e2154 · outbound

This paper cites an unresolved cited work.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:22:19.602022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:22:19.148389Z digest=sha256:322b75eaedd6ba879bb42067906c204743456a64545a735b0fc4839b6d8c6153

Observation 11324b7e-7c75-4a5f-902e-28698d6d1270 · outbound

This paper cites intent":.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation intent":

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:22:19.586212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:22:19.152777Z digest=sha256:b8465942516594912bc07b312ef40d3d8277fc156e85ebc499aace0900366a7b

Observation c0000d0d-d641-4e14-b532-3e8b8510e1ad · outbound

This paper cites The Llama 3 Herd of Models.

CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation The Llama 3 Herd of Models

Reference 2024

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unresolved
no resolver link, observed 2026-08-15T21:22:19.079364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:22:19.079364Z digest=sha256:4090f00d8360c79ad077eb559e8bc6b5f0371178419b5ca2b3b48a6931559f44

Pith citing papers

Observation 48e8e00a-c10c-4ad6-87d5-ebc1e200d6e8 · inbound

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code cites this paper.

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:21:10.770401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-08T17:37:51.790000Z digest=sha256:254b3d537dec96170312626135ee4a91343465aaa9fde90b9ae59623e7a5cfaf