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Paper Citation Record · LEDGER

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2508.18462.

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

pith.paper-citation-record.v1
2508.18462 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:28:25.425437Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:14:11.345252Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T21:38:18.790309Z

Reference resolution

37 of 37 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0b1dd94-2e62-477c-bc94-03474012e978 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 1

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raw_fallback, observed 2026-08-05T16:28:29.407492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:21.968735Z digest=sha256:fa71601acccbc61d21cb570a755c19f13d17d2dfa6a47ded523a4aba460c9012

Observation ce8656ce-43b9-4f44-a176-3df3460c700f · outbound

This paper cites Scaling Laws for Neural Language Models.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Scaling Laws for Neural Language Models

Reference 2

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no resolver link, observed 2026-08-05T16:28:22.011326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.011326Z digest=sha256:9dd7897b43cbe04bbf6cdcb5b4152a1cf42f8e8b860b6b2a7dc1a42e985eb7c7

Observation 0337f627-0707-4234-85b3-46bd78ed66b8 · outbound

This paper cites OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models

Reference 3

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no resolver link, observed 2026-08-05T16:28:22.084503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.084503Z digest=sha256:c67f8313436e6df10d3769578f2701d34776f3e0585a06af1a70e3c52173b724

Observation ca31f762-ff0e-490c-a23a-0188dab51d15 · outbound

This paper cites Qwen2.5-Coder Technical Report.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Qwen2.5-Coder Technical Report

Reference 6

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no resolver link, observed 2026-08-05T16:28:22.330173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.330173Z digest=sha256:5d7c3ec9247fcdd7ccb70dd66f019a220432276b95d6509e92eb73be82e8180f

Observation d05d32d1-33cb-4021-a3df-57fa32b5e964 · outbound

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

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 7

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-05T16:28:22.392151Z digest=sha256:231302589798d64102a416e0e6ffe294b0c0bb78d8c3927f85ff10a876c8e641

Observation 8651e4f0-f01d-461b-8a53-65ae1c5a4864 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Evaluating Large Language Models Trained on Code

Reference 8

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no resolver link, observed 2026-08-05T16:28:22.480856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.480856Z digest=sha256:6820edcf9986c01458e611b5ca72e036f01ebd4d9cbe8ca674b59b8735116301

Observation 92cf5aaa-833c-42c5-aa0b-26d4b99bfbb3 · outbound

This paper cites Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.554445Z digest=sha256:1e80da9e4ce386bf6dadbbde38cda7f24044f860199f276aabc987b4814c7948

Observation 0bf9aecc-0b43-4989-bd69-584a283099e7 · outbound

This paper cites Zheng, G.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Zheng, G

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-05T16:28:29.202146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:22.651377Z digest=sha256:e56a012fe281140e0c3b29134c4c6013e4a8833b94a5bf50c0f3858ba7037050

Observation ada31771-3cfb-470f-a593-8ea29a27ba3b · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:22.691168Z digest=sha256:3eefac0b4436bfb596a52176b55ee6362c5133bf8b1a6ed3b6a2d76fb020a7b8

Observation 00a45bff-a91a-4c97-902e-8a87f1a12d9b · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 12

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raw_fallback, observed 2026-08-05T16:28:29.049389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:22.808373Z digest=sha256:95861dc6b7bf3e9eceaa881882816f3087de57047dc41b39ca9d5ea15aed7589

Observation e491920e-dcf3-44ee-852b-ca422f9268e4 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 13

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raw_fallback, observed 2026-08-05T16:28:28.806241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:22.945339Z digest=sha256:84a52cb144134eb5b006b8879b30cd4f2de4614ab1ed38bba8f0dd1704a674c4

Observation c30c8371-5e17-4705-a149-a12752f6063d · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 14

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raw_fallback, observed 2026-08-05T16:28:28.533275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:22.989479Z digest=sha256:9ee056f6509383902ccb63ec0428bd06b3343cda622820cd4133d0d99ffa0348

Observation ea83a352-454f-448a-810f-8d2cc060d73a · outbound

This paper cites RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.056538Z digest=sha256:8f14079f582cfcc5b1f700a0092a6cb39bef711ed2450c7bd6128279e0c0fe5a

Observation 0cde8a5e-ce47-45e2-8df7-822e2965564e · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 16

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source=arxiv_source observed=2026-08-05T16:28:23.142291Z digest=sha256:f6236d1aa3cbe0153534224293214f2ba0e89983e187ef2fbab79e78c4dbafa9

Observation c1889f3e-ef72-4548-ba4c-6d72c1dcc3cf · outbound

This paper cites Nadimi, G.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Nadimi, G

Reference 17

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.283937Z digest=sha256:52180d7618437d3230f9570e4b34fc865541d04c0ad7fecf70868aa0ba7f8988

Observation 3e3c838c-6bd3-4b49-98a3-123f96906d39 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 18

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raw_fallback, observed 2026-08-05T16:28:28.410554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:23.309275Z digest=sha256:1470e484813b23481f96acc13cda7168e348d2e3e9d6b3d93976569da802713b

Observation f81278ce-87d3-4e72-8deb-4cc18a8bb516 · outbound

This paper cites CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair

Reference 19

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no resolver link, observed 2026-08-05T16:28:23.394069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.394069Z digest=sha256:a9ecdab24cde68cc8b2ee4b16cee891f7a36d1f182328ef5f480cd3ef916f835

Observation 90e026dc-a184-4fba-91a6-8254070bdacb · outbound

This paper cites HaVen: Hallucination-Mitigated LLM for Verilog Code Generation Aligned with HDL Engineers.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning HaVen: Hallucination-Mitigated LLM for Verilog Code Generation Aligned with HDL Engineers

Reference 20

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source=arxiv_source observed=2026-08-05T16:28:23.466779Z digest=sha256:82e557ffb0ab070a2383bb495b14f8c24199c6c77361a57c51df3de7506353c3

Observation d0f859d1-452e-4595-b8f6-a38ca7c3b2d1 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:23.624010Z digest=sha256:f2c6178888db0e6e0501dbdb9f002d9d079ece490ecdccfbeabd7b944c420774

Observation 7bf63cef-2a6b-462f-baf3-5fe81a863774 · outbound

This paper cites ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.667324Z digest=sha256:67be6c71d22f65b249d15f10fd530a201a0bb728e2259dd89c21439ea5a19aed

Observation feb9cb6d-254c-4435-bb56-fb5b6f0b3753 · outbound

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

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning o1-Coder: an o1 Replication for Coding

Reference 23

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no resolver link, observed 2026-08-05T16:28:23.800435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.800435Z digest=sha256:3fa8c05b989189f11d7091d7b2373487c22b2357c0aac22672321fd3e63446b4

Observation 39cfd819-272d-4632-81b6-c14fb3c770df · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 24

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no resolver link, observed 2026-08-05T16:28:23.922141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:23.922141Z digest=sha256:616d200694fe91e66a37ef262d9ce7cebe3646b6b38069e7254ad3ba0b6e71fb

Observation 4e608cb5-419b-4951-aab7-d892bb9938a0 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 25

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raw_fallback, observed 2026-08-05T16:28:28.098646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:24.028838Z digest=sha256:0bf36761455c502f6e528f7ba5dd6615430f2d594644ed1387e66ce7af88bdcc

Observation 475105f1-9590-4e5f-9d77-1958a31d5d2e · outbound

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

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.125394Z digest=sha256:f18da033b930754c70e3cf2be314c6ab33235d759542eb1cfbea777c202fb385

Observation f82f7251-46e0-41d7-9d66-00789839178d · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:24.218836Z digest=sha256:213d2a6110787c5ed1829b1af4c6d797c69474389c3cea7a36278f5e5b9950fa

Observation 5fc0b55a-aca2-4299-9980-cd5a2d4f90cf · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:24.292715Z digest=sha256:d729baab6ccc46e33a0f4b00cc83c803c7efffa16553db1cb6ad7d23b59cce05

Observation 09dc13c3-c7ea-4364-bdf8-502aeafa3a8e · outbound

This paper cites Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation

Reference 29

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.367469Z digest=sha256:975e8dd67a346d587a4b8b9eb9368a7850cab9997678c1f0fe049c6b21ad5ec1

Observation a74a5bf7-a0dd-4cc3-b120-5d3a66f8d7c3 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 30

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raw_fallback, observed 2026-08-05T16:28:27.139256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:24.478210Z digest=sha256:37c450f5da8c34b4bdaefe414427ec63c34b21b5eaab61f897552213db43da54

Observation 892a309e-02ba-4dd4-aea2-c9b5976a8fcf · outbound

This paper cites Zhang, Y.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Zhang, Y

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T16:28:26.844108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:24.617196Z digest=sha256:de3eb65857d90e258c9106ad44e06d8d6a2832900d50603a68c094014633ce2e

Observation f6dc1164-875f-48ca-a793-08ebc49ea0a5 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 32

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no resolver link, observed 2026-08-05T16:28:24.726902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.726902Z digest=sha256:40283b79e2d4ef14343a0e44f6c32ea9623fbf22e9bc94da9cbebe3c9db94e73

Observation cb3afca8-1dd9-4d81-9918-12f65db4bb6d · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 33

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no resolver link, observed 2026-08-05T16:28:24.805658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.805658Z digest=sha256:b29fe9cb0af54fccaf33601ed42e046ef1875a2bfc700d2f7cb3a9bf0ab333a4

Observation 954cfc98-b21b-401b-ba92-f5c7e113d066 · outbound

This paper cites StarCoder: may the source be with you!.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning StarCoder: may the source be with you!

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.928836Z digest=sha256:5177f97739928f65172bfd043878b421cd48711950d24f2e96928efcf52d1027

Observation 66de2aae-3a1c-4afa-9a9d-92cffd650c8b · outbound

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

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Code Llama: Open Foundation Models for Code

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:24.988529Z digest=sha256:ae666cd81ca60fe8ee02de814ed02c4f2537499968f87540f0236421cd5a93ea

Observation 6b006717-fcd9-4e1f-a184-e2b3d461966e · outbound

This paper cites Qwen Technical Report.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Qwen Technical Report

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:25.076248Z digest=sha256:9984692d66589ce55a662cee842b02f41e6d26359d26774e9d6a3a5489d87312

Observation 66784273-8fd1-4643-8000-716b39dad172 · outbound

This paper cites ChipNeMo: Domain-Adapted LLMs for Chip Design.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning ChipNeMo: Domain-Adapted LLMs for Chip Design

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:28:25.147288Z digest=sha256:052b6c177d2b6cebe78119be68ac4ee52d77400753618372a128f93758fbaae0

Observation 70376b72-9478-4bd3-9e7c-281dce07532d · outbound

This paper cites Thakur, B.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Thakur, B

Reference 38

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verified exact
raw_fallback, observed 2026-08-05T16:28:25.852746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:25.277774Z digest=sha256:8506dfec1d1dcea0c8230faa7f2be87f59f8a05636b93e371d69209676a32857

Observation 7f227235-c5d5-4df6-a2d7-9423e24631d1 · outbound

This paper cites an unresolved cited work.

VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-05T16:28:26.603736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T16:28:25.425437Z digest=sha256:f78539ef3478e93a22afc4fe6e3c5b72b180e65c249b02c998d9b093a67f56ca

Pith citing papers

Observation 4d427995-b5cf-431d-a3f8-f2b2b38207dc · inbound

TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning cites this paper.

TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-13T21:38:18.791535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T21:36:14.478007Z digest=sha256:b1d7d660112e8b71f45dc25a1cc93e901940e0c23c514b1c256c01b819f0b810

Observation 174ff88f-b81f-44d3-ae42-5af09ba88d10 · inbound

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs cites this paper.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-01T15:14:11.345252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:14:11.345252Z digest=sha256:0cd796c1f89fcf49fcbaf1fbf6157e0b885bd480c6b72508eaf8bf08f0993e77