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Paper Citation Record · LEDGER

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.00028.

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

pith.paper-citation-record.v1
2502.00028 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:58:07.029237Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy21
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7093240-ab6c-4c0a-84fe-ebfa0e99a476 · outbound

This paper cites Competition-level code generation with AlphaCode,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Competition-level code generation with AlphaCode,

Reference 1

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raw_fallback, observed 2026-08-10T16:58:07.861271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 2803b0bd-5706-476f-a255-e2aeaec39a59 · outbound

This paper cites Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.851149Z digest=sha256:0c0c05548cfa7d2deadfd8c07746d423f9c6124fc26ec5fcc907a16a23a53f96

Observation 627e2d81-9841-4654-b030-40b7441c8b33 · outbound

This paper cites Machine learning in advanced IC design: A methodological survey,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Machine learning in advanced IC design: A methodological survey,

Reference 3

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raw_fallback, observed 2026-08-10T16:58:07.845381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.856611Z digest=sha256:3a831f3b74654cd07036b2e5e46bbdf967d1c6252f7507914d3dfe10971fdbca

Observation 7e9da787-bbc1-4f5f-adb2-f6460e9d9bb6 · outbound

This paper cites LLM-Aided Efficient Hardware Design Automation.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency LLM-Aided Efficient Hardware Design Automation

Reference 4

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source=pdf_text observed=2026-08-10T16:58:06.861949Z digest=sha256:ff9270fab943c0e1f692ca45b5308760f0e24f3444f701aa7f1d933e1871a473

Observation 604e7b5f-01d4-480e-91b7-5835acb3289b · outbound

This paper cites Chang et al.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Chang et al

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.867242Z digest=sha256:1c506da5fb9968a5ac8cc79fde0f499e615584914fdf910005bd01ad9764a9d3

Observation a71f55a3-c91e-4026-b83f-0f648472c0e3 · outbound

This paper cites an unresolved cited work.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Unresolved cited work

Reference 6

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raw_fallback, observed 2026-08-10T16:58:07.829495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.872354Z digest=sha256:2b1f7130933616e1a8320e1c0805985eca10779c1e26b0050f0e2add3fb49a1e

Observation 6adb2abd-946c-474b-81c6-ae3ebb331f6f · outbound

This paper cites Chip-Chat: Challenges and Opportunities in Conversational Hardware Design.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.878152Z digest=sha256:5dd34fb9bf354cb410a06455b6d71895125be13a76c83cd789964d0f4f8953c1

Observation cf89072c-e25a-40cb-a0d6-fb9eef4ccea4 · outbound

This paper cites BetterV: Controlled verilog generation with discriminative guidance,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency BetterV: Controlled verilog generation with discriminative guidance,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.814352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.883475Z digest=sha256:b97f9095077e3378fed0efc81b2ae854f17fbbe83ffd505e002a3bd86c8da1f2

Observation e1d3f5d1-3747-4176-9e33-779c4bf5215b · outbound

This paper cites VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool

Reference 9

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no resolver link, observed 2026-08-10T16:58:06.890705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.890705Z digest=sha256:c3554263388f769712bb4cbb4623f865546de6b7a3db9ceccbb47d43a1152653

Observation e21a49e5-5924-4142-b78f-31ffcb011f8d · outbound

This paper cites Evaluating Large Language Models Trained on Code.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Evaluating Large Language Models Trained on Code

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.897536Z digest=sha256:ea32163a4c5880ec0a0651cfdbda8f8760f1f9230692ada0c76f47d8eeb3f1f8

Observation 6897fc08-5fb0-4c3d-b813-11c8c4989cb8 · outbound

This paper cites Large language models for EDA: Future or mirage?.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Large language models for EDA: Future or mirage?

Reference 11

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raw_fallback, observed 2026-08-10T16:58:07.798829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.902755Z digest=sha256:028111f2387437c68ac83dd6cd90694dde8192057b61be10b42ef72cf72ee5da

Observation 17b98f96-280d-477b-95dc-99d015c25a3b · outbound

This paper cites DA VE: Deriving automatically verilog from english,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency DA VE: Deriving automatically verilog from english,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.781765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.907673Z digest=sha256:ecc2eea79e75ea1c4ebbe3fdf542d8e9113df23d49ea4ce80567446f0e8ec175

Observation c68d8d40-6e50-4334-8b9f-157d47d58147 · outbound

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

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency ChipNeMo: Domain-Adapted LLMs for Chip Design

Reference 13

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.912772Z digest=sha256:36607946bc96e154c7beb83518430c0804d6ec13ec2fdd7b2246d5f37b887b30

Observation 588a83b6-074b-409a-aa35-62ec33f7a660 · outbound

This paper cites Benchmarking large language models for automated verilog RTL code generation,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Benchmarking large language models for automated verilog RTL code generation,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.765508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.917859Z digest=sha256:6f3ae2c0be6426fc560b50e25544a9082e4cb4764750c6c093e6f296c9f9e03a

Observation 5eab1210-ce69-40b4-9d55-d803e0ff730e · outbound

This paper cites CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization

Reference 15

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no resolver link, observed 2026-08-10T16:58:06.922350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.922350Z digest=sha256:44b59e21b1a6f838321ec25bc1a17a0378c648fdbb5cb83d54e4779c211e4271

Observation f29525f1-1b7c-47ce-8a3e-fcd18042d7b9 · outbound

This paper cites RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution

Reference 16

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no resolver link, observed 2026-08-10T16:58:06.926819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.926819Z digest=sha256:f875583bdd5e8171686172de4a30a96336c5f3d2c73cd4cc662c646a9e589c07

Observation 65a50014-beea-4df7-804b-0aec962c85b6 · outbound

This paper cites OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.931196Z digest=sha256:ee1ba95b5b78f847c7a25ed5b8ecffbe1e98de28bac785b54914fc8a3818dc1b

Observation e463ae52-0cab-4501-a644-65d0ab57883b · outbound

This paper cites AutoVCoder: A systematic framework for automated verilog code generation using LLMs,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency AutoVCoder: A systematic framework for automated verilog code generation using LLMs,

Reference 18

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raw_fallback, observed 2026-08-10T16:58:07.749227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.935452Z digest=sha256:7b1415eb27879a00fd166ebb9ee4cf6f293cc444ab1b5832f2d7157c385cbaae

Observation 5c18d573-e21e-4c62-8fda-06d49124bdb8 · outbound

This paper cites , GPT-4 technical report , Mar.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency , GPT-4 technical report , Mar

Reference 19

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raw_fallback, observed 2026-08-10T16:58:07.733825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.939706Z digest=sha256:5a7c43e81d9964034d7adaa116fa4b50c63992373dbb14f5e744a7006c6b637e

Observation 148b627a-3478-4090-b5fd-77e52848185e · outbound

This paper cites Improving large language model hardware generating quality through post-LLM search,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Improving large language model hardware generating quality through post-LLM search,

Reference 20

Resolution
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raw_fallback, observed 2026-08-10T16:58:07.719236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.947424Z digest=sha256:b58472a84589206b9c1d91a656e88ee69d9e5abd21a0e1f58751fc0781f985a5

Observation b7de1065-6401-4148-b380-cad8d9394c29 · outbound

This paper cites an unresolved cited work.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency 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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.952377Z digest=sha256:3eb17de98d490896044cdc6e31268cbe699e851ecdfed28b22a8985d2bb2023f

Observation 180622b8-9a97-4ce9-9f60-ac6c111f1654 · outbound

This paper cites Retrieval-augmented generation for knowledge- intensive NLP tasks,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Retrieval-augmented generation for knowledge- intensive NLP tasks,

Reference 22

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.957409Z digest=sha256:e17865967c402f73d407aace362d08494bcf1360246d69858f22422866d64fb7

Observation 6798ad41-e677-4613-8e06-7176355df969 · outbound

This paper cites Thakur, J.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Thakur, J

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.670833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.962516Z digest=sha256:fdaf4f2a14ef9c901d16d371db2684edbfcee2b22f96ec2b2c9965f09adcf67a

Observation dc370711-a111-4c91-8c19-638cfe9fa5a5 · outbound

This paper cites Au- toBench: Automatic testbench generation and evaluation using LLMs for HDL design,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Au- toBench: Automatic testbench generation and evaluation using LLMs for HDL design,

Reference 24

Resolution
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raw_fallback, observed 2026-08-10T16:58:07.653954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.973418Z digest=sha256:d042121fd67799a33b12d478ecd223a62c3c28b0ca5867e937ea99017f6f9c9a

Observation 16ce1470-2220-4aa7-a4df-d9ef3ede9f0e · outbound

This paper cites Self-consistency improves chain of thought reason- ing in language models,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Self-consistency improves chain of thought reason- ing in language models,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.637072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.978362Z digest=sha256:a1c19d175e0b822d83c02775c38bdfd54cd0cce2f6df2822a5de704dc3a07764

Observation 6934ff53-ea16-48f2-9a53-46a3f51c0dba · outbound

This paper cites Segmental minimum bayes-risk ASR voting strategies,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Segmental minimum bayes-risk ASR voting strategies,

Reference 26

Resolution
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raw_fallback, observed 2026-08-10T16:58:07.621372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.983640Z digest=sha256:77ee05a21b73cb99806508932ae60106744a676ecc87fd3a01a1157d914ced5a

Observation cb0aa8d3-6785-4d2d-b3a5-6c5b563a68c5 · outbound

This paper cites A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER),.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER),

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.604350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.988526Z digest=sha256:3a463a694785a060171f205a255fe491cc3862784f00301fed53b60aafe8a568

Observation fa0459dc-5512-4088-aed9-6c54de6980dc · outbound

This paper cites Minimum bayes-risk decoding for statistical machine translation,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Minimum bayes-risk decoding for statistical machine translation,

Reference 28

Resolution
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raw_fallback, observed 2026-08-10T16:58:07.588057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.993548Z digest=sha256:6678975cbbfda0b6e353bbe8d6b1b18c0038a2ae6d343e5cd5f87593ff6b742a

Observation 94083977-565e-4b73-b492-7bb840533239 · outbound

This paper cites Bleu: A method for automatic evaluation of machine translation,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Bleu: A method for automatic evaluation of machine translation,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.568961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:06.998469Z digest=sha256:3310ae9a92bf10529f23c69887662c252ec48cad2478b2820a434048d87fc49c

Observation 2b8d7650-3614-417d-8d60-ddfff06e02cc · outbound

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

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:07.003653Z digest=sha256:f948d2b258f8ab58bd573ca507365d5cf8423cdc7ac15e3764c09fd216ba99a0

Observation 3c1da549-02f4-4d88-82ef-fedac5301093 · outbound

This paper cites VerilogEval: Evaluating large language models for verilog code generation,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency VerilogEval: Evaluating large language models for verilog code generation,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.550404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:07.008615Z digest=sha256:51d40fc06405d25db6ad81ebc466d63558b499ec819907ac751cf0450388ac3d

Observation 76f96a91-7f3d-4a67-b039-6a1bfcbe0227 · outbound

This paper cites Steveicarus/iverilog.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Steveicarus/iverilog

Reference 32

Resolution
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raw_fallback, observed 2026-08-10T16:58:07.533334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:07.013489Z digest=sha256:125416b88f329e46b15f5c69b93d89cedca9facbcfa425894727c5ad7255a199

Observation 696ee5e4-257b-4165-8bce-4820eb7a17ee · outbound

This paper cites Huggingface transformers.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency Huggingface transformers

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.515703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:07.018518Z digest=sha256:6f467d81a01de80c5ef2168e46ad0d38d1a05abc52d98ed1e353c3160f8df6ef

Observation c5c7233f-b0de-4885-8766-27656872e963 · outbound

This paper cites The Llama 3 Herd of Models.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency The Llama 3 Herd of Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T16:58:07.023966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:07.023966Z digest=sha256:e2d7c3f2c2f4170cbcafce39da0d8f941bcccbd19cf211024199115932139647

Observation c94d3be8-5bcc-425e-8784-c2d7984f37bb · outbound

This paper cites AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration,.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency AWQ: Activation-aware weight quantization for on-device LLM compression and acceleration,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:58:07.499030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-10T16:58:07.029237Z digest=sha256:c500476ee0698a7895679386073f16cb2a3415d522e64e7f947bd38a51368594

Observation 00770345-3436-4d3d-9503-7c901f6f4ad4 · outbound

This paper cites AutoChip: Automating HDL Generation Using LLM Feedback.

VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency AutoChip: Automating HDL Generation Using LLM Feedback

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T16:58:06.967807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:58:06.967807Z digest=sha256:4aa359cbcb62a1548a26236a099eb133250ff30406c366ee54ea0e4dd4684e5a

Pith citing papers

No inbound Pith citation observations are available.