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Paper Citation Record · LEDGER

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2603.09161.

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

pith.paper-citation-record.v1
2603.09161 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:22:44.961261Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

28 of 28 outbound references displayed

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  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66a81c4d-bc58-4857-856c-90de6fc31f2b · outbound

This paper cites Gnn4tj: Graph neural networks for hardware trojan detection at register transfer level.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Gnn4tj: Graph neural networks for hardware trojan detection at register transfer level

Reference 1

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source=pdf_text observed=2026-08-03T01:22:41.967148Z digest=sha256:11a39f3661f890c41c9c6a9000baa7212e1ce8694f625d69e0c5e4f256f964f7

Observation 9daf0812-b22f-4297-9e3d-8e7ed1b45241 · outbound

This paper cites Graph similarity and its applications to hardware security.IEEE Transactions on Computers, 69(4):505–519, 2019.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Graph similarity and its applications to hardware security.IEEE Transactions on Computers, 69(4):505–519, 2019

Reference 2

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source=pdf_text observed=2026-08-03T01:22:42.048472Z digest=sha256:1cea4ef325bb1a8a649d4997b2ea4c99938afcfa1c15571431e20a5e38e4bb9f

Observation a41f2736-145c-4ff9-9b1b-d4a4b5164dbe · outbound

This paper cites GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment

Reference 3

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source=pdf_text observed=2026-08-03T01:22:42.194968Z digest=sha256:ee6b0724f0c83eb1dd16a6a0c382577150798da906056496c3e26006a22bc2b6

Observation bcd0d1f1-c61d-47a4-84c9-171d737fe422 · outbound

This paper cites Widegate: Beyond directed acyclic graph learning in subcircuit boundary prediction.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Widegate: Beyond directed acyclic graph learning in subcircuit boundary prediction

Reference 4

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source=pdf_text observed=2026-08-03T01:22:42.308665Z digest=sha256:08759754890d2eb829d4c6f0717f06137619116dc8d585bd108cd1980df468f9

Observation db293d11-619b-4f0a-b27e-1ab469b4c26d · outbound

This paper cites Relut-gnn: Reverse engineering data path elements from lut netlists using graph neural networks.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Relut-gnn: Reverse engineering data path elements from lut netlists using graph neural networks

Reference 5

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source=pdf_text observed=2026-08-03T01:22:42.423702Z digest=sha256:bd025c7e044763800848992b63f7e38fe78e657b6f1cdc90cc56ce3e462a6050

Observation a5cc2ac6-f180-4bf4-ad92-503d5c494be6 · outbound

This paper cites an unresolved cited work.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-03T01:22:42.561028Z digest=sha256:700a766879401e717ed1390c9911ce9d6c02d5decf3c422b1d48f1c3810c17d2

Observation 25e9a21e-1517-4ebf-a30d-955fe8cb7045 · outbound

This paper cites Functionality matters in netlist representation learning.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Functionality matters in netlist representation learning

Reference 7

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source=pdf_text observed=2026-08-03T01:22:42.631791Z digest=sha256:390f7bca406a4c439fcb8fe48a896223250ebadde72b19b9cf3898d63d881334

Observation 90ae8349-c681-4f46-ba2b-ec0a512a2801 · outbound

This paper cites Gnn-re: Graph neural networks for reverse engineering of gate-level netlists.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(8):2435–2448, 2022.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Gnn-re: Graph neural networks for reverse engineering of gate-level netlists.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(8):2435–2448, 2022

Reference 8

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source=pdf_text observed=2026-08-03T01:22:42.757793Z digest=sha256:33b551ac93ace135c5b75377ca254d3fe83840c3cb477d6f559e864c2c6d7042

Observation 0ff7c95f-616a-467a-b674-01e7fa8f2a40 · outbound

This paper cites Dagnn-re: Directed acyclic graph neural network for functional reverse engineering of gate-level netlist.Integration, 102:102343, 2025.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Dagnn-re: Directed acyclic graph neural network for functional reverse engineering of gate-level netlist.Integration, 102:102343, 2025

Reference 9

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source=pdf_text observed=2026-08-03T01:22:42.870412Z digest=sha256:3f2bdd3aca4d5575c025cf0fdb9fd64b72bd692ddc6051e535c25bb73bf4c27e

Observation 2a66bd2a-cd72-4ded-9d40-5094317efa5d · outbound

This paper cites Appgnn: Approximation-aware functional reverse en- gineering using graph neural networks.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Appgnn: Approximation-aware functional reverse en- gineering using graph neural networks

Reference 10

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source=pdf_text observed=2026-08-03T01:22:42.936407Z digest=sha256:d61241a813eaecde4c268ec586b12a45c57d6ff06245bd40d645be7d4fa2b778

Observation 80f236cc-74ca-4d1d-a8fe-d6c2e6ee67b7 · outbound

This paper cites TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Reference 11

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source=pdf_text observed=2026-08-03T01:22:43.039644Z digest=sha256:9edd999e78b18f55f64bbdc67ab99c39f7c2c853599567cb4a29e51f03204d2d

Observation 8fc43752-4168-4878-9722-0a07b861176d · outbound

This paper cites Hw2vec: A graph learning tool for automating hard- ware security.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Hw2vec: A graph learning tool for automating hard- ware security

Reference 12

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source=pdf_text observed=2026-08-03T01:22:43.199051Z digest=sha256:44950189811e510a3ae4023c08534b32fb8c36246926a828204ee35e8ece6eb8

Observation ca082758-7564-47db-a411-95e83f94053f · outbound

This paper cites Hardware trojan detection using graph neural networks.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 44(1):25–38, 2022.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Hardware trojan detection using graph neural networks.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 44(1):25–38, 2022

Reference 13

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source=pdf_text observed=2026-08-03T01:22:43.337038Z digest=sha256:65e46e366f591a03270e74b7fe18ae44118aa24bdedfd625fbafa04ba61422dd

Observation 90462282-f23a-44a0-9280-9f563276e26b · outbound

This paper cites Rtlcoder: Outperforming gpt-3.5 in design rtl generation with our open-source dataset and lightweight solution.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Rtlcoder: Outperforming gpt-3.5 in design rtl generation with our open-source dataset and lightweight solution

Reference 14

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source=pdf_text observed=2026-08-03T01:22:43.397861Z digest=sha256:43182c3b0f60aea7d9f47e8d1c79e54563b45e4c69ba7f6452e49dd4a5602a62

Observation c670c22c-67f3-4a5b-86cc-34fabb4207b5 · outbound

This paper cites Data is all you need: Finetuning llms for chip design via an automated design-data augmentation framework.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Data is all you need: Finetuning llms for chip design via an automated design-data augmentation framework

Reference 15

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source=pdf_text observed=2026-08-03T01:22:43.481427Z digest=sha256:76d9f5f392b6c0b95f7a2e5036cccebfd8e6aaaeef211938723f84e4958b0194

Observation 5f378b06-7708-4393-b8c0-fbcd7bf94af8 · outbound

This paper cites Rtllm: An open-source bench- mark for design rtl generation with large language model, 2023.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Rtllm: An open-source bench- mark for design rtl generation with large language model, 2023

Reference 16

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source=pdf_text observed=2026-08-03T01:22:43.571235Z digest=sha256:a6c396535d8a727256f37face7e36435ddf9a9cdf81717d11bfaf29dc7518503

Observation 24aaf0de-88af-4dec-97a0-9b83eb5aa7ec · outbound

This paper cites Gnn4ip: Graph neural network for hardware intellectual property piracy detection.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Gnn4ip: Graph neural network for hardware intellectual property piracy detection

Reference 17

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source=pdf_text observed=2026-08-03T01:22:43.635084Z digest=sha256:9fb40a310129e10c22dd5bdcb529705a2b34bc1c2622df2c08021291ac9bb8af

Observation 20094e10-ed1d-4e45-8710-8a621c7a63e3 · outbound

This paper cites Hansen, H.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Hansen, H

Reference 18

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source=pdf_text observed=2026-08-03T01:22:43.772180Z digest=sha256:bdc41c02c3e48aef6aced009a0a2fc7ac83d114faa979bedb33b9b0280148119

Observation 4663a317-4cb7-4d16-a4f9-cb8c4bfbbd95 · outbound

This paper cites The epfl combinational benchmark suite.Hypotenuse, 256(128):214335, 2015.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL The epfl combinational benchmark suite.Hypotenuse, 256(128):214335, 2015

Reference 19

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source=pdf_text observed=2026-08-03T01:22:43.823940Z digest=sha256:7eb4e67c1949b70c6ca26bb983abaa2c0a8abe5b8efb6eacc1e4eeba242e5c17

Observation 7b19cc1c-8812-4775-b745-cd06edf6fbe8 · outbound

This paper cites Deepgate2: Functionality- aware circuit representation learning.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Deepgate2: Functionality- aware circuit representation learning

Reference 20

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source=pdf_text observed=2026-08-03T01:22:43.933402Z digest=sha256:e70f074a432f374a7d6a9dc85d723893a40d402bd8bdaefa59bf67d9f52ca6ae

Observation 757f7e89-878d-42b7-9a58-8626e1b84ce5 · outbound

This paper cites Deepgate3: Towards scalable circuit representation learning.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Deepgate3: Towards scalable circuit representation learning

Reference 21

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source=pdf_text observed=2026-08-03T01:22:44.084577Z digest=sha256:c1c72a2eacae92e8c18e7cc44ad0d45281850c027c3b4f2f5ed6c09a92d3472c

Observation 7dca8377-599a-4c70-acb1-903a61a7a1e4 · outbound

This paper cites Functional matching of logic subgraphs: Beyond structural isomorphism.arXiv preprint arXiv:2505.21988, 2025.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Functional matching of logic subgraphs: Beyond structural isomorphism.arXiv preprint arXiv:2505.21988, 2025

Reference 22

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source=pdf_text observed=2026-08-03T01:22:44.190055Z digest=sha256:68058ddf266ee5181b33da5a93503e56cb76c6fdf9ad2c7c13681a4fdb48efe9

Observation e719f8ba-4c8c-49b4-b353-3b28dc2733c6 · outbound

This paper cites Autosilicon: Scaling up rtl design generation capability of large language models.ACM Transactions on Design Automation of Electronic Systems, 30(6):1–21, 2025.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Autosilicon: Scaling up rtl design generation capability of large language models.ACM Transactions on Design Automation of Electronic Systems, 30(6):1–21, 2025

Reference 23

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source=pdf_text observed=2026-08-03T01:22:44.340967Z digest=sha256:0954a561179c7012d8df7e1d579961b387bcf0e5147f3200c1789595860c8b9e

Observation 71385824-3fed-4c01-ae25-dbd88b5f756c · outbound

This paper cites Llm voting: Human choices and ai collective decision-making.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Llm voting: Human choices and ai collective decision-making

Reference 24

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source=pdf_text observed=2026-08-03T01:22:44.474346Z digest=sha256:96ad805513b9931ab1fc877d69693774cb9c83a4a0bacfc5f3224a340fb3a3a8

Observation 0930e047-6a28-41a9-8675-f7b3508ee10c · outbound

This paper cites Embodied LLM Agents Learn to Cooperate in Organized Teams.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL Embodied LLM Agents Learn to Cooperate in Organized Teams

Reference 25

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source=pdf_text observed=2026-08-03T01:22:44.572743Z digest=sha256:fb67947fa2cf7cf3409868f0466573d2c71fa56f2475c1fc528b94e331b3157c

Observation 38cccb8c-10eb-4714-90f6-c26b47d1ae49 · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 26

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source=pdf_text observed=2026-08-03T01:22:44.715677Z digest=sha256:5cc8e303bfd88eb4218799535837906429b17b90ba374b5b2efcfd80f0b25ad3

Observation 4a1232f9-5fc4-4be0-a2bc-e9a18b219261 · outbound

This paper cites PicoRV32 - A Size-Optimized RISC-V CPU Core.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL PicoRV32 - A Size-Optimized RISC-V CPU Core

Reference 27

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source=pdf_text observed=2026-08-03T01:22:44.888076Z digest=sha256:6b824eb3d9957384f70b7dcd69cdfa930888ae83ddf3a37b54e219b23eb5deec

Observation 7e2ddf86-e5b3-4d26-9323-e690ac5b59e0 · outbound

This paper cites NEORV32: A small, customizable and extensible mcu-class 32-bit risc-v soft-core cpu and microcontroller-like soc written in platform-independent vhdl.

Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL NEORV32: A small, customizable and extensible mcu-class 32-bit risc-v soft-core cpu and microcontroller-like soc written in platform-independent vhdl

Reference 28

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source=pdf_text observed=2026-08-03T01:22:44.961261Z digest=sha256:a3ae66406b514dd90a66ed0d275449e18df9a3b23c3b5e34bc350df37a2d4546

Pith citing papers

No inbound Pith citation observations are available.