{"as_of":"2026-08-10T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7adba150b315f5f9763fdfca14a530a01940f77fd63844aa4343bd8356190947","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:27:59.124757Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T01:22:43.039644Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.17894","snapshot_observed_at":"2026-08-03T01:22:43.039644Z","title":"Trojan-guard: Hardware trojans detection using gnn in rtl designs.arXiv preprint arXiv:2506.17894, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.09161","last_updated":"2026-07-31T10:22:18Z","snapshot_observed_at":"2026-08-09T13:45:34.586893Z","submitted_at":"2026-03-10T03:52:41Z","title":"Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T01:22:43.039644Z"},"links":{"cited_paper":"/paper/2506.17894","citing_paper":"/paper/2603.09161"},"observation_digest":"sha256:9c6d48c0052b631b0c263599a694cc39569ee3a3e595d3f00981325663f790ff","observation_id":"80f236cc-74ca-4d1d-a8fe-d6c2e6ee67b7","resolution":{"observed_at":"2026-08-03T01:22:43.039644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.17894/citation-record","integrity":"/paper/2506.17894/integrity","json":"/paper/2506.17894/citation-record.json","paper":"/paper/2506.17894"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:06.024754Z","title":"https://trust-hub.org/#/benchmarks/ chip-level-trojan","venue":null,"work_id":"2c31bf8a-299c-4d59-8a07-4ac9dd8d4ab2","year":null},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.308629Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:bfe710db8bc0a5f65276b2a8b929911f5453cbfcc8ad189c5c0339fbeee9299e","observation_id":"b26fae2a-5a1f-4255-b73c-5c5af5e9aeb8","resolution":{"observed_at":"2026-08-06T23:28:06.130174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:05.800104Z","title":"Deep learning based graph neural network technique for hardware trojan detection at register transfer level","venue":null,"work_id":"fe9bc530-485d-40b7-ac28-cca426921606","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.359359Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:742ea71ee1b800ff9179a3b9f7cc839cbfb768d0aea06c67941a5b6818a0ac2b","observation_id":"1504bc57-2caf-48c5-8a2c-34139aecec6a","resolution":{"observed_at":"2026-08-06T23:28:05.914762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:05.513525Z","title":"Gnn4ht: A two-stage gnn based approach for hardware trojan multifunctional classification.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024","venue":null,"work_id":"5161914a-3cc2-4d2f-b951-7f2973a7eb54","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.449356Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:8d185293a1e2c825745b35e8dc9876d7c35521c994e83dfb5906fbb216fade5a","observation_id":"f252d84d-8267-49ca-ac77-f49f259e6a71","resolution":{"observed_at":"2026-08-06T23:28:05.643292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:05.337087Z","title":"Security vulnerability analysis of design-for- test exploits for asset protection in socs","venue":null,"work_id":"f601809a-f031-4a1b-9a15-dc3cd1a7fb01","year":null},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.529353Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:7f3af05a0f9205b419b8eae38924a2df24cb9274637cd802d1529b298f9584ee","observation_id":"fe3875a1-ce96-4c04-97f3-bed6d93c3091","resolution":{"observed_at":"2026-08-06T23:28:05.434762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:05.063107Z","title":"Darkriscv","venue":null,"work_id":"c20376d8-8e29-4a0d-9582-0affa3633a3b","year":null},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.609627Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:a70c57f323f309c3967c1dc17a920f6ed322add82bcca4d185aed96e9007ccc7","observation_id":"bbf89a3d-4032-40c4-bbbf-d71495fa2579","resolution":{"observed_at":"2026-08-06T23:28:05.179683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14314","last_updated":"2023-05-23T17:50:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-23T17:50:33Z","title":"QLoRA: Efficient Finetuning of Quantized LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14314","snapshot_observed_at":"2026-08-06T23:27:56.704743Z","title":"Qlora: Efficient finetuning of quantized llms.ArXiv, abs/2305.14314, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.704743Z"},"links":{"cited_paper":"/paper/2305.14314","citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:b3b34e183edc13a1aede77e8b1badb44b449253e2149c54a094963218f4ea0d0","observation_id":"ab3ce9fb-101f-4e15-a9cd-60eb9d5ce8e5","resolution":{"observed_at":"2026-08-06T23:27:56.704743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03078","last_updated":"2023-06-05T17:53:28Z","snapshot_observed_at":"2026-08-08T23:08:43.190961Z","submitted_at":"2023-06-05T17:53:28Z","title":"SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03078","snapshot_observed_at":"2026-08-06T23:27:56.792601Z","title":"Spqr: A sparse-quantized representation for near- lossless llm weight compression.arXiv preprint arXiv:2306.03078, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.792601Z"},"links":{"cited_paper":"/paper/2306.03078","citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:80223ef8630970246fa8c53efd4e6548ba42ae4a084f9c6812504649e6f604eb","observation_id":"44826bbf-66e8-4734-8daa-9f17adbd79e1","resolution":{"observed_at":"2026-08-06T23:27:56.792601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:04.647362Z","title":"Llm4sechw: Leveraging domain-specific large language model for hardware debugging","venue":null,"work_id":"6497db93-4995-4633-8727-34f9bcc409bc","year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.887077Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:f1ae6dfd142ce498236ad82f24692ea6084a5baf21c333a3d74c78bd2833b46e","observation_id":"21ae4fde-7845-4b7d-93bd-8a0482db1715","resolution":{"observed_at":"2026-08-06T23:28:04.818125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:04.391596Z","title":"Node-wise hardware trojan detection based on graph learning.IEEE Transactions on Computers, 2023","venue":null,"work_id":"9c6639ae-1599-4b7b-9acc-17e2d9894639","year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:56.955426Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:bed6458dac72aca334527f07fb7c9fafa5c9e5c832a52f0a888ac179fed8dc13","observation_id":"f3bb5365-da0f-42d0-aa50-a43cd0ccdd18","resolution":{"observed_at":"2026-08-06T23:28:04.537464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:04.078612Z","title":"Graph centrality algorithms for hardware trojan detection at gate-level netlists.International Journal of Engineering, 35(7):1375– 1387, 2022","venue":null,"work_id":"e41351d6-1c3e-40d3-8374-c159a2c2570f","year":2022},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.017578Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:af8c1fb74e6d80785921c58af28641af8985f1237fca788d12c58539452752a5","observation_id":"713f0adc-bfd0-428b-8337-66227062b53a","resolution":{"observed_at":"2026-08-06T23:28:04.204826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:03.742909Z","title":"How secure is your cache against side-channel attacks? InProceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, pages 341–353, 2017","venue":null,"work_id":"6854c7c4-cc08-48ba-b8aa-bd062b89f7ff","year":2017},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.100232Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:7ceb52f06bf83ca234da3a07b95e7f86d1815702f9cfd6b6fb36d13c09b90892","observation_id":"fa7edf2e-49b0-41d4-b30a-b5d695a2d6ed","resolution":{"observed_at":"2026-08-06T23:28:03.940867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:03.589659Z","title":"Howard, Hartwig Adam, and Dmitry Kalenichenko","venue":null,"work_id":"81d93191-11d2-41e7-992d-39771c5f1df1","year":2018},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.200157Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:d4237a78bfb7a68f20851774c8e513f35b142944fbbee2f70d90ca9579f67696","observation_id":"83ed98bf-673e-40cc-8cf8-85cf1098fc15","resolution":{"observed_at":"2026-08-06T23:28:03.659509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:27:57.258564Z","title":"Kipf and Max Welling","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.258564Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:a163264e22b2a309c6ae56c1d2a5bf725976d8952cdba5ac4e4fa2dcb9abe61e","observation_id":"f0cb63fb-33d8-47ab-90df-b5b4c9384cf4","resolution":{"observed_at":"2026-08-06T23:27:57.258564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:03.292530Z","title":"Gnn-based hierarchical annotation for analog circuits.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(9):2801–2814, 2023","venue":null,"work_id":"a7098395-9070-496f-ae40-0ea9a2f99a69","year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.333006Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:2e202485e788d98e94920a25ee443294185b31e66ba4d33455e14be1d20fa661","observation_id":"8b2b2afb-782f-44ee-bd22-0b3d3d354fa1","resolution":{"observed_at":"2026-08-06T23:28:03.404978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:03.072503Z","title":"Evaluation on hardware-trojan detection at gate-level ip cores utilizing machine learning methods","venue":null,"work_id":"d2ce046f-ec28-476e-96e4-444eef74e902","year":2020},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.423947Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:ce301cb8692ce8c72ec1513002e1cfa0dfc6833b72148df6a31e3f26d2b58a7d","observation_id":"64259b09-6810-4051-918b-a3232b1059fe","resolution":{"observed_at":"2026-08-06T23:28:03.179675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:02.828664Z","title":"A reduction of a graph to a canonical form and an algebra arising during this reduction.Nauchno- Technicheskaya Informatsiya, 2(9):12–16, 1968","venue":null,"work_id":"b6b72a86-93c6-46bc-96fe-9ca0115a0319","year":1968},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.515553Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:e3888af3041a9e0375813450ab7bfc9e2d96b986655002898772bf02568fa616","observation_id":"a285f953-2d96-417c-be34-7b6c1f269349","resolution":{"observed_at":"2026-08-06T23:28:02.926681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:02.579760Z","title":"Gnn-based hardware trojan detection at register transfer level leveraging multiple-category features.IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2024","venue":null,"work_id":"1c72f1af-fccb-4051-8246-27a1da30a656","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.584751Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:d674275a92531bea2b395edbed502083f7eae7e9f630f5450d3aa4459f1e34b7","observation_id":"b9cc947f-c7c4-41b4-8ed2-81fe26acc558","resolution":{"observed_at":"2026-08-06T23:28:02.695318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:02.381869Z","title":"Online planner selection with graph neural networks and adaptive scheduling","venue":null,"work_id":"72463ec9-30d1-4fdf-90f5-b659ef549331","year":2020},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.664747Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:a22156230f7254fc9194ab40d2bc80698bdc49c220c3bec8f27e86bf11b42e64","observation_id":"c4f14950-5969-4b02-a417-3db0600b0408","resolution":{"observed_at":"2026-08-06T23:28:02.486265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:02.088656Z","title":"Security and trust vulnerabilities in third-party ips.Hardware IP Security and Trust, pages 3–14, 2017","venue":null,"work_id":"a88ed034-062c-46ab-95b1-a46745252249","year":2017},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.774921Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:e034f134ba3556afc092481acb4154f8c0dc2509c09e508f98373d2a47345eb4","observation_id":"3c978395-2db7-45f9-882f-99fdff11c69c","resolution":{"observed_at":"2026-08-06T23:28:02.230801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:01.721939Z","title":null,"venue":null,"work_id":"2390977d-deef-41c2-8158-942cd04069f4","year":2018},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.854747Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:bdf5c10c891b7aef9aae5a1c3ee92d6e2636238dcd6b1004e2cf95f04af9c62f","observation_id":"ead78610-2d34-4e89-8fc6-ec6ff4286f08","resolution":{"observed_at":"2026-08-06T23:28:01.836712Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:01.618242Z","title":"Avfsm: A framework for identifying and mitigating vulnerabilities in fsms","venue":null,"work_id":"1c3682d9-532d-4f04-bbf8-af1e8466bff6","year":2016},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:57.943330Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:29ba25d6aad07cf577c9aaae169d283b112cbda642cdb3fac133cc68f211dfcd","observation_id":"3fa0b9fb-a198-4672-888b-5faf04b25a21","resolution":{"observed_at":"2026-08-06T23:28:01.667227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:01.406554Z","title":null,"venue":null,"work_id":"bf060856-6395-4a57-953a-34e02ea3bda0","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.010656Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:10fa826472d23f35ad3e3bcb8b4b1b4e21126c3cec98db6a3eb154f0b22ba484","observation_id":"1e03fb7c-36db-4279-8c37-fd5523254b8b","resolution":{"observed_at":"2026-08-06T23:28:01.505292Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:01.101255Z","title":"Power side-channel leakage assessment framework at register- transfer level.IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(9):1207–1218, 2022","venue":null,"work_id":"e109cff8-a369-428c-9d9c-1b2ac87d8f3b","year":2022},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.084821Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:ec0daf37ee5c6b3fbee6ab3d1b0facc51536aa8cfef89728819bc4b5bcf8d801","observation_id":"413088b5-0726-4c38-bded-c899fdae2e41","resolution":{"observed_at":"2026-08-06T23:28:01.249582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.909929Z","title":"System-on-chip platform security assurance: Architecture and validation","venue":null,"work_id":"c4304e54-1117-4538-b111-92bab11e790f","year":2017},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.160243Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:e0e34b32efb7669b09e71e075918d212863f0cac5eceb1ebab0bff985a1f8bff","observation_id":"a138dc17-bf2d-4f94-9087-081495195d3b","resolution":{"observed_at":"2026-08-06T23:28:00.958796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.751946Z","title":"Llm for soc security: A paradigm shift.IEEE Access, 2024","venue":null,"work_id":"be644c79-0f90-415d-8dd1-51ebf190af1b","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.237661Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:6cfaf4e1abde9e7c8b367d83d422c156642d4233c2afd8ac6fda35bde52152dc","observation_id":"2a0ae501-03f4-4217-9044-2696d7145f0d","resolution":{"observed_at":"2026-08-06T23:28:00.827114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.585403Z","title":"Graph of circuits with gnn for exploring the optimal design space","venue":null,"work_id":"439a6a6c-247b-4a36-b31f-1efc518093ce","year":2023},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.351252Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:cf555f0a13ccdff2100c50846868b55272c0ae61100da27fd0b43ff95b02fb6f","observation_id":"c8cb6ae8-57e2-485e-b1b8-2af083bbc177","resolution":{"observed_at":"2026-08-06T23:28:00.660860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.388778Z","title":"No change, no gain: empow- ering graph neural networks with expected model change maximization for active learning.Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":"0573da2d-b653-4447-b26a-270e5be2558a","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.423765Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:98adb9306107259f15e51c967681d28b2059e19c3f8eaa82010e8fbfb2e4f742","observation_id":"00d323d2-0d6e-487e-b26a-613dbff255cd","resolution":{"observed_at":"2026-08-06T23:28:00.505076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.201555Z","title":"Pyverilog: A python-based hardware design processing toolkit for verilog hdl","venue":null,"work_id":"fd679ba0-9c2c-4c5e-a04b-4c891aa802ed","year":2015},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.498344Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:e47611b2e1c12089d661d59f8a1d1a681d4aec42bbac2d91900e96221f49a97b","observation_id":"1896717f-64d9-4504-8be4-a22c0d5c7f3e","resolution":{"observed_at":"2026-08-06T23:28:00.265226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:28:00.069131Z","title":"A survey of hardware trojan taxonomy and detection.IEEE design & test of computers, 27(1):10–25, 2010","venue":null,"work_id":"783135a5-c295-4284-ab6f-0e0f3c331c08","year":2010},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.581353Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:910fb55c359ee298cd62fe92819b0883d86ff36ffb520e988ceccabccf5b69a4","observation_id":"52639bdb-2368-4ffe-9bf8-729498b189fd","resolution":{"observed_at":"2026-08-06T23:28:00.126514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04874","last_updated":"2024-01-25T13:04:27Z","snapshot_observed_at":"2026-08-09T00:44:10.155186Z","submitted_at":"2024-01-25T13:04:27Z","title":"Choosing a Classical Planner with Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2402.04874","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.04874","snapshot_observed_at":"2026-08-06T23:27:59.271315Z","title":"Choosing a Classical Planner with Graph Neural Networks","venue":"cs.AI","work_id":"8e91633d-ee5a-40ad-8535-1e26e31bdec5","year":2024},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.656304Z"},"links":{"cited_paper":"/paper/2402.04874","citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:d305d2528019d344f72cdd6b4e488bc6aaf7aed7df6a2cd9bd6c993562bba4f2","observation_id":"e34af92f-f4ec-4359-a59f-4cb5b5343475","resolution":{"observed_at":"2026-08-06T23:27:59.312956Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-06T23:27:58.793931Z","title":"Graph attention networks.arXiv preprint arXiv:1710.10903, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.793931Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:5c5eb78c78edc872b346d73a455cd2871a8b03067ad477665f5cd8eccc2f5e47","observation_id":"78bf5f56-3442-4be0-9ab2-dd78b1a814c1","resolution":{"observed_at":"2026-08-06T23:27:58.793931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-07-06T07:05:24.565760Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-06T23:27:58.853189Z","title":"How powerful are graph neural networks?arXiv preprint arXiv:1810.00826, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.853189Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:aaad9b9dd66a70683abc6f98b158dde0e86696aecfd8088eb5bae31d1629e771","observation_id":"2ad3e9dd-8583-4283-8bcb-e8250a4fb4f6","resolution":{"observed_at":"2026-08-06T23:27:58.853189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:27:59.931709Z","title":"Hardware trojan detection using graph neural networks","venue":null,"work_id":"2d7c36cc-9a31-45fd-91ec-950bbe6cfce8","year":2022},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.940379Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:6b8fc0626b5ad6b6474da8da8719f4434e01216be2b308275606c4d78aa57248","observation_id":"8381ed76-dd89-437d-9cc2-87ec55306551","resolution":{"observed_at":"2026-08-06T23:27:59.974612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:27:59.755768Z","title":"Gnn4tj: Graph neural networks for hardware trojan detection at register transfer level","venue":null,"work_id":"4dbadb21-be0f-47ba-b6ab-47184ae69b59","year":2021},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:58.997238Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:504171126fcc54186dd4993f8801b998b87d7436b32a010728646c31e5552556","observation_id":"ae54c907-9a2b-4fe5-afc3-46755425b2f8","resolution":{"observed_at":"2026-08-06T23:27:59.817237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:27:59.597487Z","title":"Hw2vec: A graph learning tool for automating hardware security, 2021","venue":null,"work_id":"a1cf8503-85cb-4756-a6a7-393ef685f4a9","year":2021},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:59.066068Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:4e5cd230628fbf3e5bef461aff7ed0055d240b52b5790202da6b22685b4b1a8c","observation_id":"bbf447ba-f153-4522-b141-af4771379efd","resolution":{"observed_at":"2026-08-06T23:27:59.648626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:27:59.489483Z","title":"Hydrogen jet and dif- fusion modeling by physics-informed graph neural network.Renewable and Sustainable Energy Reviews, 207:114898, 2025","venue":null,"work_id":"ef09caa1-83d3-4cef-819d-bbae543c0974","year":2025},"citing_paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:27:59.124757Z"},"links":{"citing_paper":"/paper/2506.17894"},"observation_digest":"sha256:fae126b2638999749ec26279194a796761810a08a67bfcb3f386e796a51e28c8","observation_id":"61a36d0d-b3b2-45d0-91fa-7f4926dc37f8","resolution":{"observed_at":"2026-08-06T23:27:59.523906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.17894","last_updated":"2025-06-22T04:13:30Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T22:24:27.768075Z","submitted_at":"2025-06-22T04:13:30Z","title":"TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.17894."}