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Paper Citation Record · LEDGER

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review

As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.00296.

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

pith.paper-citation-record.v1
2506.00296 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:13:31.535259Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a37f985-df76-41b4-b758-d99c49668c08 · outbound

This paper cites URL: " 'urlintro :=.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review URL: " 'urlintro :=

Reference 1

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source=arxiv_source observed=2026-08-07T12:13:27.819894Z digest=sha256:52107d8450b855f44e240d4e2256e03f33ab51320f541cb70ed1203d22d95466

Observation 409171b2-fc51-410d-b2ef-322429ab9c7e · outbound

This paper cites write newline.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review write newline

Reference 2

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source=arxiv_source observed=2026-08-07T12:13:27.868037Z digest=sha256:10746bde7e11ab5634916a772451bea6d895fb30b945dbdbffdd45a2d4cb0610

Observation 1226788f-182f-4901-b966-5447b3b1dbb4 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Constitutional AI: Harmlessness from AI Feedback

Reference 3

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source=arxiv_source observed=2026-08-07T12:13:27.955998Z digest=sha256:e3f5ac267df93d3785961cd5519d79fd9d3960af8df819022372b471b97ea760

Observation 66f96338-bf22-44e1-af7e-f60e92c2f523 · outbound

This paper cites CodeT: Code Generation with Generated Tests.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review CodeT: Code Generation with Generated Tests

Reference 4

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source=arxiv_source observed=2026-08-07T12:13:28.041024Z digest=sha256:e504c4410c7e425ce4946a4fc76f1c1b32dbbcaad41ae92a169f9beac0e0b5e1

Observation d5201cf0-82e2-40c3-9b48-2f0f7f9a2223 · outbound

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

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Process Supervision-Guided Policy Optimization for Code Generation

Reference 5

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source=arxiv_source observed=2026-08-07T12:13:28.131857Z digest=sha256:c7086517d77ef1cf2ac895c06962a40796dc872bb7db51cd29bf1993d84d736e

Observation 4887bd46-fc39-4dd5-b312-36bb30a7e0e8 · outbound

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

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

Reference 6

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source=arxiv_source observed=2026-08-07T12:13:28.236569Z digest=sha256:53da0c20a0cf3d87bdb090cc7358505036ae2f42eba6e6cc1884d2db5810f71a

Observation e80f16b5-a0d7-4e28-8ca4-bc5bcf4d98a9 · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T12:13:28.338992Z digest=sha256:25824b33b84a9bfa9806b097650acc00eca760ad3348869ac99ca2dd5d38dfc0

Observation d85c020e-2b9f-4eab-839a-fdb88376e34c · outbound

This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 8

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source=arxiv_source observed=2026-08-07T12:13:28.430295Z digest=sha256:91bb0dc5116b7805fc7c0ced5eb4498ebe5c10fbfd5e6639f740cecf42ae4598

Observation c96330a2-5f5e-4403-b8c0-954be3c8e936 · outbound

This paper cites Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use

Reference 9

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source=arxiv_source observed=2026-08-07T12:13:28.578751Z digest=sha256:da567a155bf6e8a6f7fed47023bdfd61766ad0825c1b9c15580d0855a1e9a250

Observation 9a4f0a3c-c7f6-404d-8446-79094505de63 · outbound

This paper cites From Code to Courtroom: LLMs as the New Software Judges.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review From Code to Courtroom: LLMs as the New Software Judges

Reference 11

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source=arxiv_source observed=2026-08-07T12:13:28.712917Z digest=sha256:930ec41571092a55bbfb03df3f562b1ff165a98172b7152215271a068808bef9

Observation 743a6e27-1580-4f63-8809-6e8b24a4334d · outbound

This paper cites Qwen2.5-Coder Technical Report.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Qwen2.5-Coder Technical Report

Reference 12

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source=arxiv_source observed=2026-08-07T12:13:28.766693Z digest=sha256:588b77c00cceb4f7a38896d6627319dab9a2439267d436b269d1681f4b78f3fc

Observation 0891ab40-109b-405c-8711-a0ca8d557998 · outbound

This paper cites Combining Large Language Models with Static Analyzers for Code Review Generation.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Combining Large Language Models with Static Analyzers for Code Review Generation

Reference 15

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source=arxiv_source observed=2026-08-07T12:13:28.983443Z digest=sha256:e041d92ea85ce2c96e06869a789686bd0ce33f8bafee77cf43ff969f755f7095

Observation b8e9ab3b-f30f-4602-84c9-cc48cf655b2c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Adam: A Method for Stochastic Optimization

Reference 16

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source=arxiv_source observed=2026-08-07T12:13:29.031582Z digest=sha256:eb75afc01722da60ea7d0d1f49d6c21de8870b2bef00e4ac2c641cd1bf0a1bb6

Observation 85f51358-c0ed-4505-8607-ca85f02cf7b5 · outbound

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

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 17

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source=arxiv_source observed=2026-08-07T12:13:29.097459Z digest=sha256:733491cfeb198cb7cc1ba13d66a047312ce98ae0d58d8a2daf5c1d82bd30d096

Observation 2cff6e52-276b-4b3c-a851-34cd9e6bf3db · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 18

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source=arxiv_source observed=2026-08-07T12:13:29.156891Z digest=sha256:83d502b5f3b10624ef70a1853ecc9ba635e6cdd3f822249316977915c4f32ffc

Observation 258094e3-3a3b-4a44-989f-2d981cd83ba1 · outbound

This paper cites START: Self-taught Reasoner with Tools.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review START: Self-taught Reasoner with Tools

Reference 19

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source=arxiv_source observed=2026-08-07T12:13:29.255727Z digest=sha256:831eff3c010c49132d4f9c012d52348124f839d9b317401e70a6b121d56e2f53

Observation f5e3900a-9297-45a1-aaae-43a679319d82 · outbound

This paper cites Automating Code Review Activities by Large-Scale Pre-training.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Automating Code Review Activities by Large-Scale Pre-training

Reference 20

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source=arxiv_source observed=2026-08-07T12:13:29.354978Z digest=sha256:f694dad469b61818a69b7fa10a9d6bf93ae91dcc947b1a8e3fbb6128af551002

Observation 8f783172-ae83-40f5-88c7-ff51dfa864c0 · outbound

This paper cites CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models

Reference 21

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source=arxiv_source observed=2026-08-07T12:13:29.461653Z digest=sha256:7d51f778ee208543e4fdfa276a1fad78a3d613037784fe804a666584db7108ca

Observation be73da4c-922a-40dd-9db0-228a4ac094ad · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-07T12:13:29.587582Z digest=sha256:6d87b8fa511946c52be2ae1ce48f068e7a9a442bee09248e8e9e5a6b72b16d16

Observation b36489cf-d06b-4897-b3c9-75ceb9958269 · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review LLM Critics Help Catch LLM Bugs

Reference 23

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source=arxiv_source observed=2026-08-07T12:13:29.658971Z digest=sha256:f5785c6eb7ce702f0c295b9bdd172bae2bf498e5a9b87467f9ff93cf637f9361

Observation 65e25777-c9b6-4f62-9b7f-ab38f36f03f7 · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 24

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source=arxiv_source observed=2026-08-07T12:13:29.764224Z digest=sha256:81ec52ca39ac4c69ec6395684b84ec0d5797604ee461eba95db8d526e2548154

Observation 4dbb1b94-8cec-436c-a2be-f160f0de76b5 · outbound

This paper cites CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells

Reference 25

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source=arxiv_source observed=2026-08-07T12:13:29.842064Z digest=sha256:7d7d5d3cc4ce50c76034adea60016d7a13979128927955cd0ea6e775c8a9f0bf

Observation 3e89c7ac-da89-4fb0-bd0e-994a93027189 · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T12:13:29.965945Z digest=sha256:d1af78cadf4235ee71524d2eb74bba5013fcb8228c2f25d8dcaabdf9afcf7b92

Observation 5e9e2e5f-3572-4fa8-ac88-f7c9925f3aba · outbound

This paper cites GPT-4 Technical Report.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review GPT-4 Technical Report

Reference 27

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source=arxiv_source observed=2026-08-07T12:13:30.074007Z digest=sha256:43d28e93240e404c0590fbd08d32f75c85cc41afdf6596bc26e8c04519ee67c1

Observation be6c9dcb-e057-4499-9684-0710c2887e4e · outbound

This paper cites Training language models to follow instructions with human feedback.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Training language models to follow instructions with human feedback

Reference 28

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source=arxiv_source observed=2026-08-07T12:13:30.281460Z digest=sha256:1c03e5335c17948a11de66cd3f9f45069ea0693d0f08f1184613099f3fed136e

Observation ae569631-9770-4863-9f1b-e9e18ee77ac7 · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-08-07T12:13:30.389698Z digest=sha256:adb5fa071cd2f6585a0cc96bd6175e90de74dc2e82e7af5f64e62e2ab811cc1c

Observation 4ea2b124-12f3-47a5-9f30-e1082b033ea4 · outbound

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

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 30

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source=arxiv_source observed=2026-08-07T12:13:30.494844Z digest=sha256:582262a81185bf386fa80a0b4926e291ebdf4c01173626d9b7460c26aec581e0

Observation 840fba43-2526-49e2-a957-e5724f145d6b · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 31

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source=arxiv_source observed=2026-08-07T12:13:30.620350Z digest=sha256:5a77c253c862e429e8bf568dd8ca36fc728694bb95f52a2def65faedfbf1f002

Observation fa8155ff-2c86-43e0-a9c9-fa60d5157140 · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 32

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T12:13:30.739759Z digest=sha256:baf4f34a3ba79ff449069a195f6d78f077508efcf068198c8b5d90f00a35154b

Observation a6b33dcb-853a-4353-a238-205e085770ba · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-07T12:13:30.883955Z digest=sha256:214a25a655447576a817e28f2a241436c7f14a28ed387669dbbe635694683cca

Observation e4ab76c1-9d04-43bf-83d5-17e42bb72453 · outbound

This paper cites A Survey on Machine Learning Techniques for Source Code Analysis.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review A Survey on Machine Learning Techniques for Source Code Analysis

Reference 34

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source=arxiv_source observed=2026-08-07T12:13:30.996063Z digest=sha256:1171f631bfac6bf66e2aec602db079ce65879981a5f30035543078a04df3621d

Observation e5205a5a-af31-4ef0-8d55-44c38a1114d2 · outbound

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

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Execution-based Code Generation using Deep Reinforcement Learning

Reference 35

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source=arxiv_source observed=2026-08-07T12:13:31.122727Z digest=sha256:d2ddef060267e2586056e8d2b58b00e6b11ba2b87492bf9ec79ed29816e5301b

Observation 60aaf661-9633-42ec-b9f9-77c98f4f59bc · outbound

This paper cites an unresolved cited work.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unresolved cited work

Reference 36

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-07T12:13:31.209972Z digest=sha256:80801e7335c81c16af9946af56161013f2d59b52a13c2a474c87a69502433901

Observation ac31a24c-c423-4aa8-bf6f-cc421c3367c4 · outbound

This paper cites Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering

Reference 38

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source=arxiv_source observed=2026-08-07T12:13:31.407675Z digest=sha256:fac60bde9657829f41bfeacca4d83f03f13977cabd8df44fd61a74b4116a9ac9

Observation bab77ff3-568d-41f8-9017-dbb0cfde314f · outbound

This paper cites Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code.

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code

Reference 39

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source=arxiv_source observed=2026-08-07T12:13:31.535259Z digest=sha256:b7e2fd7dc567963ef3fa10e43317b89a37440f89eb26fc45ca2e96dfe56357ba

Pith citing papers

No inbound Pith citation observations are available.