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Paper Citation Record · LEDGER

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs

As of 20 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2508.07434.

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

pith.paper-citation-record.v1
2508.07434 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:09:46.562592Z

measured 59 of 59 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T10:54:54.558241Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T10:58:14.539642Z

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved41
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7dba50c2-338e-44cc-9bb5-6b51c1d6ef0d · outbound

This paper cites GPT-4 Technical Report.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.286452Z digest=sha256:80d5cafd1b6c41056adbc6d32a35268afa9b05232ef96e02be7c2311e6d9d358

Observation 729300db-ddd9-4184-b728-4c91f06656b7 · outbound

This paper cites Openhands: A n open platform for AI software developers as generalist agents.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Openhands: A n open platform for AI software developers as generalist agents

Reference 2

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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=arxiv_source observed=2026-08-05T22:09:46.292600Z digest=sha256:5c1d95416a1d31e9678aed46d7b90d6bac9d76519ca195e74b2d8ec4ed89c12d

Observation e3438dd7-8c5c-4195-b451-3770da16f3a9 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Evaluating Large Language Models Trained on Code

Reference 3

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source=arxiv_source observed=2026-08-05T22:09:46.297501Z digest=sha256:1bac4790b4be0f454c33c056b4757f620758669847d8100bc98be7872c49ef8a

Observation f6b919bd-77b7-47bb-8b7c-d14029700ffc · outbound

This paper cites Program Synthesis with Large Language Models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Program Synthesis with Large Language Models

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.302831Z digest=sha256:0ccaefba3c9726785195b1896a616f3ba020d74a8c1b53fd2ae7de8b555df6f9

Observation 554356cd-3e6a-4c0d-919d-823fd2713447 · outbound

This paper cites Less training, more repairing please: R evisiting automated program repair via zero-shot learning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Less training, more repairing please: R evisiting automated program repair via zero-shot learning

Reference 5

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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=arxiv_source observed=2026-08-05T22:09:46.308698Z digest=sha256:3e5d1cd698499b90f976f84624c0b4a4f3d72307243c8b8328fb0d885ff899c6

Observation 9f9654c3-e677-4bf9-8f1c-4840ff8fa351 · outbound

This paper cites Impact of code language models on automated program repair.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Impact of code language models on automated program repair

Reference 6

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raw_fallback, observed 2026-08-05T22:09:47.570788Z

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=arxiv_source observed=2026-08-05T22:09:46.313638Z digest=sha256:ead2ec63715ee78c8b2036594c13bee9f625c5be7a2555bd863aea02a2fef1a3

Observation c5b26618-624b-4c73-ae40-6c4b57213be5 · outbound

This paper cites Inferfix: E nd-to-end program repair with LLM s.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Inferfix: E nd-to-end program repair with LLM s

Reference 7

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-05T22:09:46.318947Z digest=sha256:e968916368fdfeeea2a9300a444369aeb22ce383e61d9b36edf4f36988cf0a79

Observation 635cfb79-3d2c-41ee-b627-7c3768874d3d · outbound

This paper cites Learning Performance-Improving Code Edits.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Learning Performance-Improving Code Edits

Reference 8

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source=arxiv_source observed=2026-08-05T22:09:46.323775Z digest=sha256:9560369cb87034362dadc97d3894c200fc221ba416eef715937c403e59c19fc6

Observation fc33c1c9-2b9f-4797-978a-b6af6ad0a5bc · outbound

This paper cites Large Language Models for Compiler Optimization.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Large Language Models for Compiler Optimization

Reference 9

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.328597Z digest=sha256:b1afe898d0c9b40e2699c7160534f8b75e62a7a30d869aec1a5a630a29de30e9

Observation f63cd078-23f0-47d5-ae2b-bc77dcebcd72 · outbound

This paper cites Tree of thoughts: D eliberate problem solving with large language models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Tree of thoughts: D eliberate problem solving with large language models

Reference 10

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raw_fallback, observed 2026-08-05T22:09:47.536801Z

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=arxiv_source observed=2026-08-05T22:09:46.334298Z digest=sha256:9b6e788862fd41e3697e4364a47411df30f41c20f226fc68bfdcf43eae04fb48

Observation be11a8e5-52af-49c7-8aa1-0af6b1c0f252 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.338954Z digest=sha256:f02a5c653110cc932f83726006d21edd3334c2b90decd4957d89a3a6c08b5b03

Observation 84845823-462d-4b71-adb7-056d11f47575 · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.343818Z digest=sha256:3b4f0c514fbac515a6c69e1e51a866f0cbb500035c41847023a43e94b8e61db2

Observation 22076380-86b1-435c-9ce2-0aebe7c31ed4 · outbound

This paper cites Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Reference 13

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source=arxiv_source observed=2026-08-05T22:09:46.348559Z digest=sha256:aa162984384e475f5418cc5612d44b81691d59a3185a5675d60c09c5d1e37c04

Observation f62a5e84-cd25-49b3-838a-0db214bd6c67 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.354061Z digest=sha256:4bc9c9c6655959eb17a11cfb1efd6a0c1e4d6a70226ba25c7b7269a6ab3bd0c4

Observation fadd8d32-6e73-48cb-84c1-7b9dd454e1ff · outbound

This paper cites Let's verify step by step.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Let's verify step by step

Reference 15

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raw_fallback, observed 2026-08-05T22:09:47.522088Z

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=arxiv_source observed=2026-08-05T22:09:46.358809Z digest=sha256:e0118344c8949df51deb499f7b769c8f53eeba5e4ad2f46459dcc298a8d9a153

Observation e5f8e6a8-aeb2-460a-8468-f03655f8a426 · outbound

This paper cites Using anytime algorithms in intelligent systems.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Using anytime algorithms in intelligent systems

Reference 16

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raw_fallback, observed 2026-08-05T22:09:47.507122Z

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=arxiv_source observed=2026-08-05T22:09:46.363199Z digest=sha256:fb9ba4c32ae704edb8b23ccee0aec576af155643ab08a0527652516d5eb2297c

Observation e4b4d4fd-d855-419b-bcc6-841a31dfd9b1 · outbound

This paper cites CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models

Reference 17

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source=arxiv_source observed=2026-08-05T22:09:46.368200Z digest=sha256:77c92c5bbd26b2be5087e2cbc459c0a3f1c8052697f683ac2425a1856523e80d

Observation 128fcbf7-3888-40bf-bb96-8ca28f1c4ab4 · outbound

This paper cites Scattered Forest Search: Smarter Code Space Exploration with LLMs.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Scattered Forest Search: Smarter Code Space Exploration with LLMs

Reference 18

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source=arxiv_source observed=2026-08-05T22:09:46.374893Z digest=sha256:f7ee87b1b6475c8730ac0ffa041d5c9fca52a61be01c6b3dfd48f9c08d87c594

Observation 3e7c7666-4e82-4b45-b29b-0b4165eb7439 · outbound

This paper cites What Makes Large Language Models Reason in (Multi-Turn) Code Generation?.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs What Makes Large Language Models Reason in (Multi-Turn) Code Generation?

Reference 19

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source=arxiv_source observed=2026-08-05T22:09:46.379986Z digest=sha256:b21e68cea64c866158d2101a8aef19fd1f467fb66578498ffd609e0d72e4ee11

Observation 0b6471bc-d9c8-464b-8914-7f4d1d361cde · outbound

This paper cites Agentless: Demystifying LLM-based Software Engineering Agents.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Agentless: Demystifying LLM-based Software Engineering Agents

Reference 20

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source=arxiv_source observed=2026-08-05T22:09:46.385268Z digest=sha256:46fcbd29e913dd011f5c28e7723cccbea4cf9da106d3cd3227620e9fd564e1c3

Observation 70354d8b-f5e8-4b42-97b8-6ff12d4096fc · outbound

This paper cites Is Self-Repair a Silver Bullet for Code Generation?.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Is Self-Repair a Silver Bullet for Code Generation?

Reference 21

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source=arxiv_source observed=2026-08-05T22:09:46.390474Z digest=sha256:d844006d108880e06d18e1f9692dbe410f295a69c88b3a00fb6d74189bcc7955

Observation 1daf6d9f-f489-4564-8362-245540113197 · outbound

This paper cites Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

Reference 22

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source=arxiv_source observed=2026-08-05T22:09:46.395536Z digest=sha256:914001556faa0647337adac1654c4dcedbd8eac8565096c4619cd868a53a9311

Observation 01558576-367b-4284-b271-d980d69ad7ce · outbound

This paper cites RethinkMCTS : R efining erroneous thoughts in monte carlo tree search for code generation.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs RethinkMCTS : R efining erroneous thoughts in monte carlo tree search for code generation

Reference 23

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source=arxiv_source observed=2026-08-05T22:09:46.399883Z digest=sha256:a3462717b4d05e0380dc25b3098931e8cc2ac2e975141aae0c89df34b2571877

Observation 248089c0-3e20-41c5-bb02-5032ab573b68 · outbound

This paper cites Artificial Intelligence: A Modern Approach.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Artificial Intelligence: A Modern Approach

Reference 24

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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=arxiv_source observed=2026-08-05T22:09:46.404189Z digest=sha256:ecf2f599270b59511845de15326125ac62a8d2d7c2ace3998194c21ee6cd6239

Observation 2a838034-9db4-4ed8-94e3-4f0691c0f2a4 · outbound

This paper cites An Introduction to Genetic Algorithms.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs An Introduction to Genetic Algorithms

Reference 25

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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=arxiv_source observed=2026-08-05T22:09:46.409159Z digest=sha256:10756aa8e1a5d4ad04c3e45f4f7494c64090bb8715fadda5e9b1b9f80318ca4b

Observation dd839d15-b014-4c9d-8050-9a0b7585881e · outbound

This paper cites Rank analysis of incomplete block designs: I.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Rank analysis of incomplete block designs: I

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.413705Z digest=sha256:f77f5c14f6529f25cec9f3d98f033ec2c4462daa21474ec0079beaf4c705f2b0

Observation 21f0e52b-0251-4d10-9b74-16b4ff93ed32 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 27

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source=arxiv_source observed=2026-08-05T22:09:46.418362Z digest=sha256:1b0123e8b912fbd3ca5ccff1f8b07891ed558478a0e4279b22f2f69b33796422

Observation a1db59e0-b932-48fc-bd81-c713a38520f9 · outbound

This paper cites TACO: Topics in Algorithmic COde generation dataset.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs TACO: Topics in Algorithmic COde generation dataset

Reference 28

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.423661Z digest=sha256:2b0bfc18be65201e89a0361aed949f0e69aef31e19ea895a722d8778ada0d887

Observation 4375161a-494f-499c-87c4-02c9b7d1c412 · outbound

This paper cites General local search methods.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs General local search methods

Reference 29

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raw_fallback, observed 2026-08-05T22:09:47.453737Z

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=arxiv_source observed=2026-08-05T22:09:46.428572Z digest=sha256:fda554dccf6ffb8fa087187c65e1181ac3be167f4df46c27f5b24ba5ebf40184

Observation ba761c82-2742-4e55-9535-f21adb5bad26 · outbound

This paper cites Policy Filtration for RLHF to Mitigate Noise in Reward Models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Policy Filtration for RLHF to Mitigate Noise in Reward Models

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:09:46.433018Z digest=sha256:676eba00d947da488f3a8401cfeb95b47a6ecba88dc8d8cd43be0b780cf5c239

Observation f86c948d-a7f0-485a-b715-10f5f230b717 · outbound

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

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Training language models to follow instructions with human feedback

Reference 31

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raw_fallback, observed 2026-08-05T22:09:47.437800Z

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=arxiv_source observed=2026-08-05T22:09:46.438795Z digest=sha256:5d0937bbc9551a85994e9d8e5c50f4b38aa353a51fc77ac46168edf17c53f260

Observation 7b5312fa-4c6c-4e8f-a20a-7d63bfcb1dc7 · outbound

This paper cites Iterated local search: F ramework and applications.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Iterated local search: F ramework and applications

Reference 32

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raw_fallback, observed 2026-08-05T22:09:47.418912Z

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=arxiv_source observed=2026-08-05T22:09:46.443559Z digest=sha256:53d7327247b7c0c349bec56fda6ee72eff0a388f7797f4ed76e8f45a11f151e2

Observation 06ce58c5-aa98-4c66-b303-560a7be1d3fd · outbound

This paper cites Course of Theoretical Physics.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Course of Theoretical Physics

Reference 33

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raw_fallback, observed 2026-08-05T22:09:47.403877Z

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=arxiv_source observed=2026-08-05T22:09:46.448843Z digest=sha256:7cc03a93a727c80d01e0feef1c1109734ead7788bdc0e39df1dc6c769522c910

Observation c8d8a287-7cff-4ed2-af20-ea17be86bce0 · outbound

This paper cites Simulated annealing: F rom basics to applications.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Simulated annealing: F rom basics to applications

Reference 34

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raw_fallback, observed 2026-08-05T22:09:47.387834Z

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=arxiv_source observed=2026-08-05T22:09:46.453733Z digest=sha256:15d8efa3f7c67ba571777d8e83c4d4b9404709eafac23b987964673ae5d19c03

Observation 9cd941a5-7c51-4a54-af53-688159c04ad6 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Measuring Coding Challenge Competence With APPS

Reference 35

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source=arxiv_source observed=2026-08-05T22:09:46.458405Z digest=sha256:4a37981b6c4b94daab3b672360db055549896f14af1c3edef483f505b0b96b0f

Observation fed6ee51-7eb3-48cf-bb47-566e73e7e7c9 · outbound

This paper cites TRL : Transformer reinforcement learning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs TRL : Transformer reinforcement learning

Reference 36

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source=arxiv_source observed=2026-08-05T22:09:46.462996Z digest=sha256:22dbf0fb03d9178e151e92a1f9e13be72967738064a823ec675fdfe93a5954b8

Observation 9e24064b-a19d-4e79-95ad-59d55fbcdff4 · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Reasoning with Language Model is Planning with World Model

Reference 37

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source=arxiv_source observed=2026-08-05T22:09:46.467293Z digest=sha256:89ac0bd697ed8376a53850e5cd80ee4187ee3aa749189e8d44ed3828e28d1acb

Observation 84e13dcf-c0ca-4438-8094-e81927529126 · outbound

This paper cites Reflexion: L anguage agents with verbal reinforcement learning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Reflexion: L anguage agents with verbal reinforcement learning

Reference 38

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

source=arxiv_source observed=2026-08-05T22:09:46.472541Z digest=sha256:3a3bc16bb453afc2292f4c3ddcc60cd8525db6a84e44423c5c7ae6c5547beeeb

Observation e4819110-3a3e-42f0-a5e3-247f8c70cdf4 · outbound

This paper cites Planning In Natural Language Improves LLM Search For Code Generation.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Planning In Natural Language Improves LLM Search For Code Generation

Reference 39

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source=arxiv_source observed=2026-08-05T22:09:46.476577Z digest=sha256:bace247d77a747344f28991c8cab75dc5d3c1b73cce802f1e8701c23d95ce429

Observation 1988d942-00da-4538-9f1a-f66590333066 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Training Verifiers to Solve Math Word Problems

Reference 40

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source=arxiv_source observed=2026-08-05T22:09:46.481131Z digest=sha256:e69860a529714af463e46b70cfbd44a975636931b515fc25fce9c4f3cbf6d3c0

Observation 17a017c2-4a37-4946-8c7f-74c9554f0182 · outbound

This paper cites Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

Reference 41

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source=arxiv_source observed=2026-08-05T22:09:46.486332Z digest=sha256:69fe64fe9e645e22ddf1f3f21ca11a986283fc5f05da8803246b8aa9705eeee8

Observation de768fc2-29dc-496b-a8e5-3320719f5fed · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 42

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source=arxiv_source observed=2026-08-05T22:09:46.491142Z digest=sha256:c85226eb8c1d0a93301668a8ac7d01089b535fc4b6ff36a0016ae5758ece0f07

Observation ad2cf4a6-4514-4084-8741-0a36004e956e · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 43

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source=arxiv_source observed=2026-08-05T22:09:46.495797Z digest=sha256:62f35c62113de655db523682b5b2a5d16186996922b14abdc38096e0c1b56c80

Observation 5694f089-1000-4f3c-8212-63184f1e1131 · outbound

This paper cites PanGu-Coder: Program Synthesis with Function-Level Language Modeling.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs PanGu-Coder: Program Synthesis with Function-Level Language Modeling

Reference 44

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source=arxiv_source observed=2026-08-05T22:09:46.500170Z digest=sha256:56a3f0f7071c2ff5b8018ab2487e10d0589f53b4e8e9ae6cd9fc9110fd9183ce

Observation a84ee40d-19f9-4ad5-998c-9987e17e9902 · outbound

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

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 45

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source=arxiv_source observed=2026-08-05T22:09:46.504731Z digest=sha256:8b52bf24cb91edbf8b3ba3567c36c86492634a6ebd479785bab6012cd952e4e8

Observation 2278c954-4689-44af-b7b0-99984abbe5da · outbound

This paper cites Qwen2.5-Coder Technical Report.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Qwen2.5-Coder Technical Report

Reference 46

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source=arxiv_source observed=2026-08-05T22:09:46.509739Z digest=sha256:ec2d5a7858f59c9105b236342538cb312ade0226da1753fa50efaf1d6d99acb8

Observation 78515099-a3ab-4044-832b-423e53ada154 · outbound

This paper cites Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Reference 47

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source=arxiv_source observed=2026-08-05T22:09:46.514342Z digest=sha256:9b7a47ab10c654b9b67d001e3da616bccbacd2ac5a31271e0c0a82cb0b1655b7

Observation 138b9b54-5a36-400d-9835-0d97c8b54655 · outbound

This paper cites Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step

Reference 48

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source=arxiv_source observed=2026-08-05T22:09:46.518940Z digest=sha256:133cf2f84e2ca803c8672177154da2e2f6dd37d2367564b331306c9c99af1694

Observation 97a66095-5eaa-4988-950c-c688b2e02cc1 · outbound

This paper cites CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges

Reference 49

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source=arxiv_source observed=2026-08-05T22:09:46.523430Z digest=sha256:d138832da873782cfc3a43b9aa7689b5acd6354a6075b47628e586549b28d357

Observation 3e9c2c79-849a-4d6e-bda5-edaa726a7580 · outbound

This paper cites SWE-bench-java: A GitHub Issue Resolving Benchmark for Java.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs SWE-bench-java: A GitHub Issue Resolving Benchmark for Java

Reference 50

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source=arxiv_source observed=2026-08-05T22:09:46.528699Z digest=sha256:496d98ad766948481b86e519821268251278d4abe60172a020e9c5a14e7913e9

Observation fc704216-e457-4e35-b9ae-a245b4274197 · outbound

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

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 51

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source=arxiv_source observed=2026-08-05T22:09:46.533667Z digest=sha256:1e42d9dd26dabcd2f4a01ee424d8ca467ac62b9b5ff5f76af231d80a9902ed0e

Observation 954858a4-2436-4a32-8a10-662f822aeaa5 · outbound

This paper cites The Lessons of Developing Process Reward Models in Mathematical Reasoning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 52

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source=arxiv_source observed=2026-08-05T22:09:46.538453Z digest=sha256:689b9d07285c23f3139782e11f82f3999ebb7e544d81ca47f7309c62e0efe2c0

Observation a1bcfbea-21e6-46dd-96b8-9aa36ae0c15b · outbound

This paper cites Enhancing LLM Reasoning with Reward-guided Tree Search.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Enhancing LLM Reasoning with Reward-guided Tree Search

Reference 53

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source=arxiv_source observed=2026-08-05T22:09:46.543585Z digest=sha256:eb31f5b29b0bdfcfcc1c4c1ba8299db2d91f4bfcd9191ed02758422d7be84710

Observation 7a4484db-d459-44f1-b5f9-e9e0afa93d63 · outbound

This paper cites Generative Reward Models.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Generative Reward Models

Reference 54

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source=arxiv_source observed=2026-08-05T22:09:46.548724Z digest=sha256:c27f0859ec3f7eaa0227a5256dcced741f4d952fd50efa48a7750defa0efeb37

Observation 48faffc0-1935-46f9-a8d2-a314fba9378f · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs LLM Critics Help Catch LLM Bugs

Reference 55

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source=arxiv_source observed=2026-08-05T22:09:46.553605Z digest=sha256:de37ff62cd4f655d2d363897cd6fc762fa9b60b08b3e7cb4e1cc1ef27d495032

Observation 418bb1bb-72bd-43d4-95ae-8614a08c161c · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 56

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source=arxiv_source observed=2026-08-05T22:09:46.557994Z digest=sha256:dfb9cf46f1997b1feeb1de06fe109c040e7ae0c393d1039bcbb22dcb40d2d51f

Observation 39c87926-b511-4da3-940d-bf9cb662250f · outbound

This paper cites Rm-r1: Reward modeling as reasoning.

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs Rm-r1: Reward modeling as reasoning

Reference 57

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source=arxiv_source observed=2026-08-05T22:09:46.562592Z digest=sha256:dfa8c4eb0083445079ebeb41680cceb779f34de2092e7211a6ff7e6635eaf9fd

Pith citing papers

Observation 12c0d974-183c-4e46-8a98-88d9002d58cf · inbound

AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search cites this paper.

AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs

Reference 36

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arxiv_id, observed 2026-05-11T08:40:57.630376Z

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

source=arxiv_source observed=2026-05-10T16:35:16.056397Z digest=sha256:6ff95263ce8d34a1414007a1069d2e7ed72cce408dc960a79ac0c0254cafa01e

Observation 8f8f53c8-5c95-48a3-bea3-182e33473d17 · inbound

Code as Agent Harness cites this paper.

Code as Agent Harness Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs

Reference 164

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

source=pdf_text observed=2026-05-20T10:54:54.558241Z digest=sha256:65e806612ef43fcfde67ce6f1331920fce0fb07b30f0e4ead7bd237c31212837