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Paper Citation Record · LEDGER

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2607.18519.

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

pith.paper-citation-record.v1
2607.18519 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:14:12.820102Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

37 of 37 outbound references displayed

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  • unresolved37
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 2e7217d6-0a67-4c06-8ebf-cf6259a24198 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 1

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source=pdf_text observed=2026-08-01T15:14:07.393241Z digest=sha256:fc000e9fb0400e4d55f21b216b6a0304c6dc337e4bfe82f4d9d45acf3b464583

Observation 9216e046-780c-494f-b1e6-49799cb8c8cb · outbound

This paper cites ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-01T15:14:07.521389Z digest=sha256:81f692671c6efc82a80f70cad1442342d17d67db98680954c11510fb466d23ba

Observation 674566bb-f673-4551-95f6-2a9dee002336 · outbound

This paper cites SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design

Reference 3

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source=pdf_text observed=2026-08-01T15:14:07.703079Z digest=sha256:31570127be73c0e6b8484a03da90cb30124b7415baad64026463eb98aa438897

Observation bb988a77-a737-459a-a205-4406b1b913ab · outbound

This paper cites HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL

Reference 4

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source=pdf_text observed=2026-08-01T15:14:07.817816Z digest=sha256:b1ec9cd758c59a04cd67b793eca9a2bf1a1bfeee6bb8c12036d5ef47ae427f11

Observation dacfc319-dc93-49ba-b839-33efbcb8a193 · outbound

This paper cites Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Lorecast: Layout-Aware Performance and Power Forecasting from Natural Language

Reference 5

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source=pdf_text observed=2026-08-01T15:14:07.949106Z digest=sha256:507094c789ea5c2547a500c76139c380dbbf5e6bbfc65b1365f9918d9ef6f9cb

Observation 95a63cc0-226e-4a3a-8e25-67824024296a · outbound

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

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs VeriOpt: PPA-Aware High-Quality Verilog Generation via Multi-Role LLMs

Reference 6

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source=pdf_text observed=2026-08-01T15:14:08.080737Z digest=sha256:12a2b9364d6a8f94577fb20043089ccd841eccd30aa2f909a35c791bba9813aa

Observation 9ca06918-6dad-4ca7-8c3a-c635438c53a2 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-01T15:14:08.217872Z digest=sha256:ef54ee4f4563bb68c6473ae45e4cd87cc9920a9a9fc3779ae94eed521b751b7f

Observation dc824629-bea8-437c-9813-9a024cd2f868 · outbound

This paper cites VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designs

Reference 8

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source=pdf_text observed=2026-08-01T15:14:08.339342Z digest=sha256:2957740ad34a257d2af90fc1fc9e83295b2145335799dce5d849fdf18bd0fe8f

Observation 9d0840b7-0517-4f72-bf98-67f684ac7785 · outbound

This paper cites Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form

Reference 9

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source=pdf_text observed=2026-08-01T15:14:08.515995Z digest=sha256:2b09b0aae2b156a717a25b4ff59de2297f1c2b53243f4df1deae8322433527ce

Observation 2ab44f95-b489-4c65-b1ed-f519c8970ca5 · outbound

This paper cites Abstractions-of-Thought: Intermediate Representations for LLM Reasoning in Hardware Design.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Abstractions-of-Thought: Intermediate Representations for LLM Reasoning in Hardware Design

Reference 10

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source=pdf_text observed=2026-08-01T15:14:08.698321Z digest=sha256:062005e102a9714f1498dc3f749f2cea73e4b3f9df5c921fbe8dbaab38bc1bbc

Observation 85db7099-3a60-476f-95e5-0859342f8015 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-01T15:14:08.808527Z digest=sha256:35f8d4365910cc71d73a7c79191c214b92c91a58ba5c3435cf0c14ab1f5ec51d

Observation bc39b5c6-f78e-4d96-8e2a-ca939a8b7f77 · outbound

This paper cites From Natural Language to Silicon: The Representation Bottleneck in LLM Hardware Design.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs From Natural Language to Silicon: The Representation Bottleneck in LLM Hardware Design

Reference 12

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source=pdf_text observed=2026-08-01T15:14:09.045687Z digest=sha256:e23ece83b45a701fd41277c6156f257c2fa7712c7e7605c195d6a02e6fd6e91a

Observation 57b4523f-46df-4a3e-9eb7-314bb9dedfc6 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 13

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source=pdf_text observed=2026-08-01T15:14:09.201514Z digest=sha256:420599e5f9555efb069aab5155e243e5d0697497f10dd1059cc5c6a002a08231

Observation 0f5b084a-3eef-4454-872e-7eda4e8d3c92 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-01T15:14:09.390240Z digest=sha256:f102b1ba1cad7b598c3e167f4a27c34d96e31bcb138d780fc9189202294437aa

Observation 1a3b745f-0b98-43a7-b38c-9f5c029bb83d · outbound

This paper cites SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip

Reference 15

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source=pdf_text observed=2026-08-01T15:14:09.660598Z digest=sha256:1afca939f662008e052168b13a6a664a2961db20cc27b597ee40c45dce63b928

Observation b17f3a37-d7e0-4099-b032-6dffdbd62362 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-01T15:14:09.820065Z digest=sha256:b1d362a242c077e03e380605e23542facc3df709c8d9c4153f20aef3ca4e7d3a

Observation 1ea5fa0f-71d0-43a1-ac9b-7773c4e905f5 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-01T15:14:09.944723Z digest=sha256:20be0180c3e49207a7e9269639f7144a6bc0e63f8e7018b6192a964426532cc3

Observation 390532dc-74c7-4949-9ef9-913104731f2b · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-01T15:14:10.076147Z digest=sha256:40bf830f8c1fd1fe4c4e982b66830e03e185ff95fb523928e9729e04ad3f48c3

Observation f7bda274-0105-40af-9963-f906113e0188 · 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.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs 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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source=pdf_text observed=2026-08-01T15:14:10.263615Z digest=sha256:87c7afa3a0c867c19a37eb7ba26a3b3a6da79c053913823dd890109780ea3174

Observation 38e487f1-7e1f-4023-b565-213c27043dbd · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-01T15:14:10.456132Z digest=sha256:3b027979a7035d61a014a8ec37f2b9596270d3f244dad0d81f4c6c2baac16499

Observation dcb741c7-cff9-4950-9f93-ae623c66a507 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-01T15:14:10.682488Z digest=sha256:76d92ee045c0b90a4ac864aa3af0b90e3a5d5a19d157c9b618ff06f23f528dbf

Observation e9acc4c0-7104-4903-ac2f-38da49ae631f · outbound

This paper cites VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation

Reference 22

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source=pdf_text observed=2026-08-01T15:14:10.781795Z digest=sha256:b3363c237e043bfaa26bc4c39b22d53110374359fda71205746d2ba6a7ec3a37

Observation b5b3f273-b238-4eeb-992c-20db2b954d25 · outbound

This paper cites In2024 IEEE LLM Aided Design Workshop (LAD).

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs In2024 IEEE LLM Aided Design Workshop (LAD)

Reference 23

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source=pdf_text observed=2026-08-01T15:14:10.552996Z digest=sha256:100d60d22eca409824ad37356b8db8d5dc6cefc1417a3c8aa0ca1df822ac3a85

Observation 3d7ee864-ebd9-487f-987d-96fdb7070ac2 · outbound

This paper cites Paradigm-Based Automatic HDL Code Generation Using LLMs.

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

Reference 24

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source=pdf_text observed=2026-08-01T15:14:11.153759Z digest=sha256:fe95cf15bb50ebc08b10723cd0764888d4307756a8d45b0daaed2596613604ab

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

This paper cites VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning.

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

Reference 25

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source=pdf_text observed=2026-08-01T15:14:11.345252Z digest=sha256:86b8284928d16a343cc8fb2678964433e172c96145d1c4c2d466e4f2b28a7029

Observation c068915c-c93e-48e1-8d8b-14ec626531ee · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-01T15:14:10.939545Z digest=sha256:6640ecbd36bddb9103bd302406374f77c0c24b1b58610969a0fd5cd86cff9bae

Observation ab754f08-a62c-47d1-9883-98313771a904 · outbound

This paper cites Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback

Reference 27

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source=pdf_text observed=2026-08-01T15:14:11.555202Z digest=sha256:c5c402ae57a11f9fccab3d1b90a020ffb469d20fa5284379c93f76621d120be7

Observation f619fb03-c0d3-4384-a6ff-b73966b20a55 · outbound

This paper cites VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs VeriReason: Reinforcement Learning with Testbench Feedback for Reasoning-Enhanced Verilog Generation

Reference 28

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source=pdf_text observed=2026-08-01T15:14:11.677747Z digest=sha256:c554bf00e8c5b23bdee08660821442f73230eba713966c2f8ea568890b1636c2

Observation f501840f-07c2-4848-b241-f4c142912544 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-01T15:14:11.426132Z digest=sha256:c247fc0ade02bf5b278f6b9d05bf35ba103e00135059d1f544460c63be51366e

Observation 821d6856-3da0-4bbb-8afa-ca7adaa9273c · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 30

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source=pdf_text observed=2026-08-01T15:14:12.123644Z digest=sha256:de11050ae48f76b5502d3f63c512efed5d4687847db028351037d8b8abd9422d

Observation 10f88c34-b255-494e-8d8a-91a449f5a91b · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 31

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source=pdf_text observed=2026-08-01T15:14:12.323793Z digest=sha256:e6f8b818f817f0e41d35dc9f4527061cd617ae52408196e82f2402b7df24a0ae

Observation 665383ad-5687-468b-aa3a-a2dd11246492 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 32

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source=pdf_text observed=2026-08-01T15:14:11.825930Z digest=sha256:024266e993fc0a9a49e49b297e4250df3119e3629b8c3d51ff9ab5348956a587

Observation ddb73a30-d6d8-4a67-a867-95aec15842d8 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 33

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source=pdf_text observed=2026-08-01T15:14:12.743580Z digest=sha256:3ec4199ca91fbe7028c56776e969b9d7c03309f1146c1a742487e8764952cb9d

Observation 1b3d6c88-3283-4d21-90c3-26ae5e207261 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 34

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source=pdf_text observed=2026-08-01T15:14:12.820102Z digest=sha256:5674ccf2b3fdfcf9ae3ed749471c2dee06ab9626bab350c9ab7ae4a19c84a2a2

Observation a4a11ad8-d484-460b-9788-ba95e63ad752 · outbound

This paper cites an unresolved cited work.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Unresolved cited work

Reference 35

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source=pdf_text observed=2026-08-01T15:14:12.553415Z digest=sha256:90693c53757aa010fe3c88ea68af68fe206de0785482f4280edccc8d1910dcd6

Observation c6458aa0-cb15-4751-8e19-3a403b9895b4 · outbound

This paper cites Abstraction-of-Thought Makes Language Models Better Reasoners.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs Abstraction-of-Thought Makes Language Models Better Reasoners

Reference 2024

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source=pdf_text observed=2026-08-01T15:14:09.511601Z digest=sha256:223d8b73db7e39db9769a9d0cedb9b2547b9ec8f6b3b4986e297b75125088888

Observation 33850d85-f626-446a-932c-b76da9938d46 · outbound

This paper cites ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation.

A Progressive Approach to Synthesizable RTL Design Generation Using LLMs ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation

Reference 2025

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source=pdf_text observed=2026-08-01T15:14:08.913455Z digest=sha256:e996952dd05ee814d317f09974f3eecd0b36138cadd2b3aa847669eef0b7f992

Pith citing papers

No inbound Pith citation observations are available.