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Paper Citation Record · LEDGER

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 5 inbound Pith citation observations for arXiv:2601.17178.

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

pith.paper-citation-record.v1
2601.17178 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:27:42.318740Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:13:22.669056Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T14:05:46.811014Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 499e9a8a-3e2f-4946-814e-2284bdc03dca · outbound

This paper cites Hardware trojan detection using path delay fingerprint,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Hardware trojan detection using path delay fingerprint,

Reference 1

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source=pdf_text observed=2026-08-03T08:27:42.142958Z digest=sha256:066c30be2a92372338e3db433bec0c0c070672e91bf44dcd9316e20bcbbd49bb

Observation dc1c1d6c-1366-4311-984b-eaf40e8c81b7 · outbound

This paper cites Scalable detection of hardware trojans using ATPG-based activation of rare events,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Scalable detection of hardware trojans using ATPG-based activation of rare events,

Reference 2

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source=pdf_text observed=2026-08-03T08:27:42.148341Z digest=sha256:372b29ea45303e2076991990c9fdbcdecf334b09ae63e9c46a5f8b80f3efbdf0

Observation 2f0a1ce0-f9d6-4f39-8ae0-bdb2fea916b5 · outbound

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

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Gnn4tj: Graph neural networks for hardware trojan detection at register transfer level,

Reference 3

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source=pdf_text observed=2026-08-03T08:27:42.153465Z digest=sha256:f4fa875d251d7532939a883dc8b2fdbabb4fb7d3f618c58bbdc7e3f10f25fd26

Observation fdcf136d-af8d-4189-996a-675ea0d5a13e · outbound

This paper cites HW2VEC: A graph learning tool for automating hardware security,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion HW2VEC: A graph learning tool for automating hardware security,

Reference 4

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source=pdf_text observed=2026-08-03T08:27:42.158576Z digest=sha256:b206347e1a74ec64e152c5838f42bcc66f965e99b5951ce579b6690c50ec9915

Observation 6615f884-f048-4f28-a237-4cc5d18d2b0f · outbound

This paper cites Graph neural networks for integrated circuit design, reliability, and security: Survey and tool,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Graph neural networks for integrated circuit design, reliability, and security: Survey and tool,

Reference 5

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source=pdf_text observed=2026-08-03T08:27:42.163402Z digest=sha256:f7b78c7f4aedebad277241284b72f14c55a4479bf587dd922b4231d4ddbbd6f8

Observation 53aa64e9-97fb-421e-a776-cc7a6e43c10e · outbound

This paper cites Trust-hub chip-level trojan benchmarks,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Trust-hub chip-level trojan benchmarks,

Reference 6

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source=pdf_text observed=2026-08-03T08:27:42.168448Z digest=sha256:43a9d7b987da22770c819628fa28b94abbf4dc4f36aa6c34ae3c5192d2c6ab51

Observation 54352cc4-21f8-4ae5-8048-90c754fdaf53 · outbound

This paper cites Sand: A self-supervised and adaptive nas-driven framework for hardware trojan detection,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Sand: A self-supervised and adaptive nas-driven framework for hardware trojan detection,

Reference 7

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source=pdf_text observed=2026-08-03T08:27:42.173150Z digest=sha256:a45c3bd9eb3f5bbdaf3afa704d746647f2451353569da351fe02175c29561f4b

Observation 90729a0c-573d-42bc-b3e6-6109fd8ca2f7 · outbound

This paper cites Attrition: Attacking static hardware trojan detection techniques using reinforcement learn- ing,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Attrition: Attacking static hardware trojan detection techniques using reinforcement learn- ing,

Reference 8

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source=pdf_text observed=2026-08-03T08:27:42.179129Z digest=sha256:8152c8d609e7e7023bef52b91e5fc4d9359a55ef36fd645acbf9056e7d7d7663

Observation 6e4028e1-2a5c-4a6f-a9ad-4d206001d12f · outbound

This paper cites TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-03T08:27:42.184229Z digest=sha256:0e791fcb7d5f1fdba8c481262e79af49d823e84be527aee1ef4dca445ef0d068

Observation e1652a65-7dcf-4f67-ad5f-a6380690d62b · outbound

This paper cites Netdetox: Adversarial and efficient evasion of hardware- security gnns via rl-llm orchestration,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Netdetox: Adversarial and efficient evasion of hardware- security gnns via rl-llm orchestration,

Reference 10

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source=pdf_text observed=2026-08-03T08:27:42.190160Z digest=sha256:6a2ee75663cd955a6c3258947083427c18cfb0c042fa36c4674f072eb954e2cb

Observation d6daadef-eec9-4483-ab6f-7e37b50e68e3 · outbound

This paper cites Automatic Hardware Trojan Insertion using Machine Learning.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Automatic Hardware Trojan Insertion using Machine Learning

Reference 11

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Observation 2e5f7ae7-9976-4d17-8a3c-caa78d5ea4ae · outbound

This paper cites An automated configurable trojan insertion framework for dynamic trust benchmarks,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion An automated configurable trojan insertion framework for dynamic trust benchmarks,

Reference 12

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source=pdf_text observed=2026-08-03T08:27:42.201508Z digest=sha256:6ed2f4da29ce3de15472f105b2a5a50ce37e8fbf1cff5a773a7de0d0a0effefa

Observation 94c57602-0acc-49b4-aeac-d57d0cedefe3 · outbound

This paper cites Taint: Tool for automated insertion of trojans,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Taint: Tool for automated insertion of trojans,

Reference 13

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source=pdf_text observed=2026-08-03T08:27:42.207009Z digest=sha256:75ccf5f2adcb1e7eb75b0438a076c9cef32aa9a167b546d4a4432f40219f8f75

Observation c627bb8e-0b8d-4017-8f95-0a01789bff8d · outbound

This paper cites Feint: Automated framework for efficient insertion of templates/trojans into fpgas,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Feint: Automated framework for efficient insertion of templates/trojans into fpgas,

Reference 14

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source=pdf_text observed=2026-08-03T08:27:42.213267Z digest=sha256:b7727fbdb2d84d3f49f11cb600d74fe55fb51b26d40b51b22c89b7ee6eea47ad

Observation 815eb94f-39e0-4534-88b6-01c246061e9f · outbound

This paper cites Verigen: A large language model for verilog code generation,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Verigen: A large language model for verilog code generation,

Reference 15

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Observation efabe223-fe81-467d-b67c-7d2b92b3ad16 · outbound

This paper cites Llms and the future of chip design: Unveiling security risks and building trust,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Llms and the future of chip design: Unveiling security risks and building trust,

Reference 16

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Observation f7298ec9-3e16-473f-9a8c-5bd110d51238 · outbound

This paper cites Lockforge: Automating paper-to-code for logic locking with multi- agent reasoning llms,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Lockforge: Automating paper-to-code for logic locking with multi- agent reasoning llms,

Reference 17

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Observation 96c046c4-4493-4b00-b58d-3fdba5d37074 · outbound

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

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Chip-chat: Chal- lenges and opportunities in conversational hardware design,

Reference 18

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Observation a7f30305-1458-4a63-b28c-cfdb46d53e12 · outbound

This paper cites DeepRTL2: A Versatile Model for RTL-Related Tasks.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion DeepRTL2: A Versatile Model for RTL-Related Tasks

Reference 19

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source=pdf_text observed=2026-08-03T08:27:42.239186Z digest=sha256:7eb7b2176a321f9251449062f531a205bf29764e8da8c976fd19ad22f07467e0

Observation 0ac6c3d6-7e0c-476b-9761-e443274c728b · outbound

This paper cites VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination

Reference 20

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source=pdf_text observed=2026-08-03T08:27:42.243958Z digest=sha256:c42f991e214e67c277ea34d265f5f02a3119e4940b1aeee2dc2a205c5f0e9207

Observation 03d9f9d4-6958-4ad4-9555-d60eff837e66 · outbound

This paper cites Gllamor: Graph-based logic locking by large language models for enhanced robustness,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Gllamor: Graph-based logic locking by large language models for enhanced robustness,

Reference 21

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source=pdf_text observed=2026-08-03T08:27:42.248819Z digest=sha256:e608a7743f407a5b326209c323bf19932ce3a245e776d754141b3ba4fea8f732

Observation 982ddc5e-a566-405b-a35d-1d0eb7bf9e33 · outbound

This paper cites VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding

Reference 22

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Observation 38d14b54-9841-4eba-8148-1d083bd58498 · outbound

This paper cites SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions

Reference 23

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Observation d048fb4e-1537-4924-9906-9a008c734862 · outbound

This paper cites Hardware trojan dataset of RISC-V and Web3 generated with ChatGPT-4,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Hardware trojan dataset of RISC-V and Web3 generated with ChatGPT-4,

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-03T08:27:42.263106Z digest=sha256:9b50513e22b2e963b5279f234c019f8a22a985d9726575536471b337930c2d3d

Observation cfd055e3-041e-4392-a2a9-4c727aa18455 · outbound

This paper cites Unleashing ghost: An llm-powered framework for automated hardware trojan design,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Unleashing ghost: An llm-powered framework for automated hardware trojan design,

Reference 25

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Observation 85615c42-45d2-4ee7-8df0-3465f66fca2c · outbound

This paper cites Dtjrtl: A configurable framework for automated hardware trojan insertion at rtl,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Dtjrtl: A configurable framework for automated hardware trojan insertion at rtl,

Reference 26

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Observation cabf6074-42cf-4ee2-9d16-d53a97e9d8a6 · outbound

This paper cites Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection

Reference 27

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source=pdf_text observed=2026-08-03T08:27:42.276959Z digest=sha256:6677d0c03dbdf898ac0dd012447d542480729c84c0b2e10ca72b51533240aa55

Observation 0aa4f397-b386-436b-a3c8-a623aac1b844 · outbound

This paper cites {AttackGNN}:{Red-Teaming}{GNNs}in hardware security using reinforcement learning,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion {AttackGNN}:{Red-Teaming}{GNNs}in hardware security using reinforcement learning,

Reference 28

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source=pdf_text observed=2026-08-03T08:27:42.282693Z digest=sha256:549b80c7be1be14bd90a48ecee36ec92e148bb235e91616753c59a05a671b179

Observation 133b9f77-9f67-44ea-bfa2-a0c85669389e · outbound

This paper cites Trojansaint: Gate-level netlist sampling-based inductive learning for hardware trojan detection,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Trojansaint: Gate-level netlist sampling-based inductive learning for hardware trojan detection,

Reference 29

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source=pdf_text observed=2026-08-03T08:27:42.286951Z digest=sha256:6446d0327ac87d9524c33e9e2f095b410f736d59cae9283ca47c3c952755662e

Observation 8f0a26e0-4b31-4808-86aa-1a36601fd917 · outbound

This paper cites Rtl-breaker: Assessing the security of llms against backdoor attacks on hdl code generation,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Rtl-breaker: Assessing the security of llms against backdoor attacks on hdl code generation,

Reference 30

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Observation 6ee7e2e5-46f2-439a-ae33-ea806bde4736 · outbound

This paper cites Salad: Systematic as- sessment of machine unlearing on llm-aided hardware design,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Salad: Systematic as- sessment of machine unlearing on llm-aided hardware design,

Reference 31

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source=pdf_text observed=2026-08-03T08:27:42.295836Z digest=sha256:82d54b4ecbb3518e9cdc10e17f7e51647e9d41b24457977fab7089227410d184

Observation 220ba3ef-e75e-43fa-af9f-2691dccdfd58 · outbound

This paper cites TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans

Reference 32

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source=pdf_text observed=2026-08-03T08:27:42.300254Z digest=sha256:08692424c0b196f80a181cbcf298a7b5717c618b9bfdd58cd5a8bbd5e6db9245

Observation c4dcaca7-3c43-4666-a8e9-5e2c76cc5a20 · outbound

This paper cites Netlam: An automated llm framework to generate and evaluate stealthy hardware trojans,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Netlam: An automated llm framework to generate and evaluate stealthy hardware trojans,

Reference 33

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Observation 08e89058-e501-43aa-a4e9-dfc8c037fb0b · outbound

This paper cites Latent: Leveraging automated test pattern generation for hardware trojan detection,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Latent: Leveraging automated test pattern generation for hardware trojan detection,

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:27:42.309357Z digest=sha256:1b5cd90ad65a2b1325cceaf99584eed2baeb572c160b03dc62fbf96a188fb693

Observation 2c74c824-ed85-4b9f-934e-4bc690a84f99 · outbound

This paper cites Trojanloc: Llm-based framework for rtl trojan localization,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Trojanloc: Llm-based framework for rtl trojan localization,

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-03T08:27:42.313919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:27:42.313919Z digest=sha256:7b22ee6417e8eac06c739ecb6a879829a2bde897cb70d64d1041c4b8f790c83e

Observation 09e9c4e5-ff47-45dd-b5ec-d3e748016bf2 · outbound

This paper cites Verigen: A large language model for verilog code generation,.

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion Verigen: A large language model for verilog code generation,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-03T08:27:42.318740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:27:42.318740Z digest=sha256:2efa29706ca52c28f1d76a53eb19f10d42684e93a040ceadd3e8a3f18eecfc77

Pith citing papers

Observation cf27f86d-59e7-4ce8-a63b-7f3db6fe648c · inbound

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges cites this paper.

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-06-23T04:13:44.037980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T03:57:51.577486Z digest=sha256:ace2e6c435eec16a2b2caa921b591005a790808f5001cc460f9f93fd3eb31a0b

Observation 812cde26-71c7-4116-b5df-7bd29e2e140b · inbound

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges cites this paper.

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-23T04:13:44.037980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T21:12:08.821202Z digest=sha256:676750997e9ea8be73b243a12c19fa73bd3851846411a90fd0bcb78799c4e9e0

Observation 848bdbb9-b9fb-458a-8c6f-9f0fbb239357 · inbound

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges cites this paper.

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-23T04:13:44.037980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T08:58:25.469021Z digest=sha256:e12f5288634807e1341944bc3be65a59781abdd030b8c6da79139aeaef95b9a7

Observation 1ab5e979-061e-4155-93b6-f7f20f78af06 · inbound

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges cites this paper.

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-07-01T14:05:46.812375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T22:18:09.663488Z digest=sha256:2ec40905306c52ca77d212db43313becd063e8b2df24ed8106a2d7c29510f1ed

Observation 596b3393-bc63-4202-910b-a7718bb178f9 · inbound

Hardware Design and Security in the Era of Chiplets and LLMs cites this paper.

Hardware Design and Security in the Era of Chiplets and LLMs TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-06T10:13:22.669056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:13:22.669056Z digest=sha256:190d4ee1bf5192d66d765f43ecbb1f293e96f534aa4a6aea1a50867fb7a43074