Pith. sign in

Paper Citation Record · LEDGER

Paradigm-Based Automatic HDL Code Generation Using LLMs

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2501.12702.

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

pith.paper-citation-record.v1
2501.12702 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:56:25.028777Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:51:34.468268Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:51:34.763865Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy20
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7f87e6d-2ce2-4bde-9a47-27d31be645af · outbound

This paper cites Llm- aided efficient hardware design automation,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Llm- aided efficient hardware design automation,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.665954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.881725Z digest=sha256:820fd2a77565606d4ee1de04983dacf977ac0768b4b90f779c3ea1d071f4ff39

Observation ccfe25ac-1b55-4d92-821f-b72962823dbd · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs Machine learning in advanced ic design: A methodological survey,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.649951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.886806Z digest=sha256:af8444a45cc78ce2e4c4cbdb7fdfccf42a3bbaac3bc92af031fa6b6b7965eecf

Observation 32a413d4-82d4-4ab8-8236-aedcf249c6e0 · outbound

This paper cites Prompting large language model for machine translation: A case study,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Prompting large language model for machine translation: A case study,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.633964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.891152Z digest=sha256:1ecca35618c13b181090e4d0df22d62e6fb84b9fed8d8a3ebb0c1b93eb514b26

Observation 154f7708-458e-4c7b-a45f-cd0e813b69a4 · outbound

This paper cites Tidybot: Personalized robot assis- tance with large language models,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Tidybot: Personalized robot assis- tance with large language models,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.895339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.895339Z digest=sha256:7e92b4748c874adfb62b7b2758116d4f118ba68128defea82f35fbe3d9d6bb35

Observation e6de3add-4cfb-406f-a476-b6aff5eaa049 · outbound

This paper cites The programmer’s assistant: Conversational interaction with a large language model for software development,.

Paradigm-Based Automatic HDL Code Generation Using LLMs The programmer’s assistant: Conversational interaction with a large language model for software development,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.607167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.899702Z digest=sha256:7757e92c44789f32b598671a6ee5746a2d5c984ddb63ffe2f1ecbcc7b99091b3

Observation 56da2583-5eb0-47b3-bef3-ee1b29c479d5 · outbound

This paper cites Autobench: Automatic testbench generation and evaluation using llms for hdl design,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Autobench: Automatic testbench generation and evaluation using llms for hdl design,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.592283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.904083Z digest=sha256:562df6af96be8d8fc6c5d41472cc5aa8f311185e4a16da9e636aa6383083bb4e

Observation e3248a29-ee1a-432d-bfce-fe3f433b9541 · outbound

This paper cites Au- tomated c/c++ program repair for high-level synthesis via large language models,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Au- tomated c/c++ program repair for high-level synthesis via large language models,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.577264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.909116Z digest=sha256:9d364d4cc1f452f48eeee59ddd149d26329074e093263692efd452c86c172115

Observation 72e33f9a-7ce0-4f1c-b6da-d604b68801a2 · outbound

This paper cites Cor- rectbench: Automatic testbench generation with functional self-correction using llms for hdl design,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Cor- rectbench: Automatic testbench generation with functional self-correction using llms for hdl design,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.563089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.913409Z digest=sha256:61392b48294f3463aef4f17cbd0c0ff3f079ee9e178cb4ffb125ab599f268bb6

Observation 15b9f622-73aa-4d49-b19b-e986aad896f9 · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs Machine learning in advanced ic design: A methodological survey,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.549474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.918102Z digest=sha256:dc0fb1da1d4912fec3eb751203b7d46f77e62949540e86250a575b1f53fad219

Observation 5a783b11-d5b6-4a85-8082-64d46494dfcd · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs Benchmarking large language models for automated Verilog RTL code generation,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.536295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.922110Z digest=sha256:d5a889e6441ed0a8a9bc05761b36bcaf9a7226b680585b11d180dc29fc9d0a93

Observation 306b5856-5d08-42fe-9795-c877e6fb19f4 · outbound

This paper cites CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification.

Paradigm-Based Automatic HDL Code Generation Using LLMs CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.926034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.926034Z digest=sha256:b79f685e3fda97d8dd834851534fa3aaa50af58ad217e815b62fa0f4f745fd62

Observation 3ec8528f-7fc4-4c7e-a48f-dd3cc3c7821a · outbound

This paper cites Chip-chat: Challenges and opportunities in conversational hardware design,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Chip-chat: Challenges and opportunities in conversational hardware design,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.522213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.930515Z digest=sha256:409029971bc882005e4664782adb58d147f7b8c6f0695e2899f6f913f699d855

Observation 048ec5db-0bc3-4810-9337-7d7c54c608c7 · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs ChipNeMo: Domain-Adapted LLMs for Chip Design

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.934730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.934730Z digest=sha256:8be6d9fadeeb6b8c1f2d84ba21fdec4348ce8394aee70bda135dbef63c407442

Observation 5a8946fd-69f0-46a4-a104-b45bfc2fd493 · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.939610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.939610Z digest=sha256:7d0decbca2bc82632fd05b28763ccf7713ef4961ad76ef9db136aecaeb9d5f89

Observation 5ad6252c-8bb7-4e89-a9db-c423f26d0e1e · outbound

This paper cites VeriGen: A large language model for Verilog code generation,.

Paradigm-Based Automatic HDL Code Generation Using LLMs VeriGen: A large language model for Verilog code generation,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.508590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.944514Z digest=sha256:6363dcaa55c0e1430ca7efaf502a59a092bff5aeca63e1d22e45fb6ba5676756

Observation f1b89990-05e1-436d-8da3-6cd395464b61 · outbound

This paper cites BetterV: Controlled Verilog Generation with Discriminative Guidance.

Paradigm-Based Automatic HDL Code Generation Using LLMs BetterV: Controlled Verilog Generation with Discriminative Guidance

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.948769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.948769Z digest=sha256:84956dd81e9cdbd70861b6cbb113e7a2fa78e5e72cc38d1b29958c6ceddc3686

Observation f67785e7-a57a-4001-8599-d7732befea2e · outbound

This paper cites Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework.

Paradigm-Based Automatic HDL Code Generation Using LLMs Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.953362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.953362Z digest=sha256:b69cc7a7c99d32686d1c1f4fa51c0013f912f4f5a2c40e740579c3cd450c1a37

Observation 7e13f782-467f-42db-9370-2e5196326770 · outbound

This paper cites GPT-4 Technical Report.

Paradigm-Based Automatic HDL Code Generation Using LLMs GPT-4 Technical Report

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.957909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.957909Z digest=sha256:509a8ec1ab4f47cc01b7d743b44b2cb6899cac3809f822f527ac88df4b6e647c

Observation c0479448-b951-425c-aa51-ec2a8742bdbd · outbound

This paper cites Rethinking the role of demonstrations: What makes in- context learning work?.

Paradigm-Based Automatic HDL Code Generation Using LLMs Rethinking the role of demonstrations: What makes in- context learning work?

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.494942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.962670Z digest=sha256:0e973c9c744693b442b42559c33740bfeee5fe38a7c9a9a90bcb3967b761c8ea

Observation 3e8eb1dc-d81d-40bb-b68a-b69639071828 · outbound

This paper cites Generation- augmented retrieval for open-domain question answering,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Generation- augmented retrieval for open-domain question answering,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.481498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.966955Z digest=sha256:6adf3c46a7444fc4069076c386c80acbc81eb0bc22be40ddacd1b0e876c8ae68

Observation ee923d0d-106a-4cf2-99e7-3988d44d92d6 · outbound

This paper cites GPT4AIGChip: Towards next-generation ai accelerator design automation via large language models,.

Paradigm-Based Automatic HDL Code Generation Using LLMs GPT4AIGChip: Towards next-generation ai accelerator design automation via large language models,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.466467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.971699Z digest=sha256:e11b2d2168fc90e4105fafa3d374891527273f537d3b0e3bfaace4bdee328ad0

Observation 09405cb9-79db-40bb-bd3b-7e96b459fb00 · outbound

This paper cites RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models.

Paradigm-Based Automatic HDL Code Generation Using LLMs RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.976202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.976202Z digest=sha256:e55ad504a1f7eace8b834d33478d3050575c39b035d1e9181a19727628298246

Observation c00e55f1-787e-437f-9976-5035768fb84d · outbound

This paper cites HDLdebugger: Streamlining HDL debugging with Large Language Models.

Paradigm-Based Automatic HDL Code Generation Using LLMs HDLdebugger: Streamlining HDL debugging with Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.980840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.980840Z digest=sha256:8d2add1544c2d25cf5eb0800dea97d6928fe29169c265f3eefc10070865aa98a

Observation de03a1db-27e3-46c4-b3e4-1bc2bf78edbb · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs AutoChip: Automating HDL Generation Using LLM Feedback

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.985879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.985879Z digest=sha256:2d6cc0cc2a33a97c21c64d3fd06d4c6dc6decccd1cc4c75a31437008aba51012

Observation c5afa7c1-52e0-4c26-afbd-04787ef3989e · outbound

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

Paradigm-Based Automatic HDL Code Generation Using LLMs VerilogEval: Evaluating large language models for Verilog code generation,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.450882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:24.990596Z digest=sha256:542a1af5d040dc04a6f1124de1ec6a5f43fcf120e0f8764485ee1c5a67c6ee42

Observation 2ee4facd-2769-43bd-b3b7-8cfd541e1edc · outbound

This paper cites Do Large Language Models Latently Perform Multi-Hop Reasoning?.

Paradigm-Based Automatic HDL Code Generation Using LLMs Do Large Language Models Latently Perform Multi-Hop Reasoning?

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.995196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.995196Z digest=sha256:96283099665ca719b87b916f42e5ca25415c3f65d9f00fb5f25633a1b58bb1fd

Observation bf808edf-1b51-4bfc-8e85-3ad957092da7 · outbound

This paper cites Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process.

Paradigm-Based Automatic HDL Code Generation Using LLMs Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:24.999856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:24.999856Z digest=sha256:76eae0c198b7764b43609ffc25ea40b8a201c7acc8eece1244ee05f105e33c0d

Observation b5695a7f-7741-40ff-a643-b95410af5770 · outbound

This paper cites Chain-of-Thought prompting elicits reasoning in large language models,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Chain-of-Thought prompting elicits reasoning in large language models,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.433716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:25.004526Z digest=sha256:a8514dfee6be5775aac1ad548f8fc8b2379c0cc8cb7332fb1ae459d861dffbcb

Observation 343f4688-8b86-475a-9d66-4ac2494f7117 · outbound

This paper cites To Believe or Not to Believe Your LLM.

Paradigm-Based Automatic HDL Code Generation Using LLMs To Believe or Not to Believe Your LLM

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:25.008733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:25.008733Z digest=sha256:0b2d7b52d0625e2a902d72f5496c4573d10a2a362ad1380344141a511b647a43

Observation 2bc00af6-c1ed-4809-971d-4ab31c613412 · outbound

This paper cites Coding techniques in verilog for finite state machine designs in FPGA,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Coding techniques in verilog for finite state machine designs in FPGA,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.418671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:25.012901Z digest=sha256:a78ff9ee7114f56bf9eb100ddaba2381bf14c41bf5ffce2ea9a1f4def576e179

Observation e3e9fbf2-62be-4a57-afa0-c2a9f26c8620 · outbound

This paper cites GPT-3.5 turbo: Language models,.

Paradigm-Based Automatic HDL Code Generation Using LLMs GPT-3.5 turbo: Language models,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.403035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:25.016879Z digest=sha256:1d7547835a88f7ec462933e851b46636c1293574d0de13446975a615b0bcdd2b

Observation 43d87bab-65c7-46bb-9785-6f54e72c0df5 · outbound

This paper cites GPT-4o mini: advancing cost-efficient intelligence,.

Paradigm-Based Automatic HDL Code Generation Using LLMs GPT-4o mini: advancing cost-efficient intelligence,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.387575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:25.021005Z digest=sha256:a97c6d882848dfbf57bd23b9407c13119f738de8485f95d1462f2851d05ecff4

Observation d2c75609-4dbf-40da-bcb4-5b2f363dea8a · outbound

This paper cites Icarus Verilog: open-source Verilog more than a year later,.

Paradigm-Based Automatic HDL Code Generation Using LLMs Icarus Verilog: open-source Verilog more than a year later,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T16:56:25.371571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T16:56:25.024900Z digest=sha256:1c6afe69768831b72387ddbac43118f8e7cd03a8557971b71805475dbbf05f32

Observation a614c6b9-1cf8-4f97-af9b-cc890f1acc2d · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Paradigm-Based Automatic HDL Code Generation Using LLMs Evaluating Large Language Models Trained on Code

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T16:56:25.028777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:56:25.028777Z digest=sha256:928a59b983a4d8bde74d06c83f2e9b0d4d73051cf4f7c1502c679920698da529

Pith citing papers

Observation b92a209b-ba91-484e-b4fd-738d23884364 · inbound

VeriOpt: PPA-Aware High-Quality Verilog Generation via Multi-Role LLMs cites this paper.

VeriOpt: PPA-Aware High-Quality Verilog Generation via Multi-Role LLMs Paradigm-Based Automatic HDL Code Generation Using LLMs

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:51:34.767015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Paradigm-Based Automatic HDL Code Generation Using LLMs

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