{"as_of":"2026-08-12T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ecee34d026410759d9c4a7d9f2743fda424e2572d94b3ae26e99690f81653a4f","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:08:28.521321Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2507.06538/citation-record","integrity":"/paper/2507.06538/integrity","json":"/paper/2507.06538/citation-record.json","paper":"/paper/2507.06538"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:08:25.294570Z","title":"Generalizing from a few examples: A survey on few-shot learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:25.294570Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:5a4cf3c7c63f6af8d0a1663f54aee80eb4c240d4a68decce1780f773ba809940","observation_id":"dd15d230-0374-4cee-bb92-8cfd857e1ce3","resolution":{"observed_at":"2026-08-06T19:08:25.294570Z","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":"2026-08-06T19:08:33.976529Z","title":"Pretraining graph neural networks for few-shot analog circuit modeling and design,","venue":null,"work_id":"55109f25-f626-40cb-a8f6-15c6249f3ed1","year":2022},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:25.431020Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:9d9f51f7124721f15b442dfd6ddb02abcf047afdaf0650770ce74426c9ea5e9f","observation_id":"4dce347e-07b6-4b7f-87ba-a6a0346dab9f","resolution":{"observed_at":"2026-08-06T19:08:34.121492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:33.685824Z","title":"Yu and X","venue":null,"work_id":"832a84dd-32c3-4054-a877-9694033510d5","year":2014},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:25.605111Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:9d03e32f6f3c834a7abe3e21df7a5effecf6d8034872eef35755201fb8f41613","observation_id":"565a88e5-3c16-420f-b789-29be30f3ef48","resolution":{"observed_at":"2026-08-06T19:08:33.822100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:33.394456Z","title":"Variational capacitance extraction of on- chip interconnects based on continuous surface model,","venue":null,"work_id":"0693ba69-6c44-46c2-88ba-31900e65decc","year":2009},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:25.760414Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:ea4bc7110efce6c5d60bb4228ecf4980e4e4dae09365e833f41b01bd5729d92e","observation_id":"e28377cf-ad84-41c3-b40d-a44e191b2b44","resolution":{"observed_at":"2026-08-06T19:08:33.519730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:25.879226Z","title":"Link prediction based on graph neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:25.879226Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:2e00d537ee7177073731dbe6a5488c91f647fe51129a1b4641c16641db1cdd8c","observation_id":"ab34f8ee-3826-436d-bdae-dc4bd8ca0cc0","resolution":{"observed_at":"2026-08-06T19:08:25.879226Z","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":"2026-08-06T19:08:33.075604Z","title":"Recipe for a General, Powerful, Scalable Graph Trans- former,","venue":null,"work_id":"e93b2e54-b68a-4d85-8f8b-9ba019ab4f63","year":2022},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.009841Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:af85ce2c3b26938d653df0dafd54476a781b64c391dd086ac95ac82fffd8e08d","observation_id":"fd072b60-650e-48dc-9847-850cfb700989","resolution":{"observed_at":"2026-08-06T19:08:33.155048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:32.722374Z","title":"Optimization as a model for few-shot learning,","venue":null,"work_id":"94677a82-0696-4383-9d47-25a6bbbab626","year":2017},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.170285Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:244050e4f0841ae2fcd763c948a5f30c260540d82c957d95bfc86303fc5e5e22","observation_id":"df5b2e37-68cc-46cb-910b-27f0fbfe9585","resolution":{"observed_at":"2026-08-06T19:08:32.903619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:32.445997Z","title":"Weisfeiler-lehman neural machine for link prediction,","venue":null,"work_id":"71e57476-b108-4022-8bbd-d5f04a3b83a1","year":2017},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.339127Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:deb117f2a31aaba012c097ba34d080806abe654b583b5bdd70a0d699f2bb5c97","observation_id":"f566a85e-0453-43a5-ab74-af96ec683f08","resolution":{"observed_at":"2026-08-06T19:08:32.577227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:32.175710Z","title":"Link prediction in complex networks: A survey,","venue":null,"work_id":"16118e65-5d28-406e-8729-749e29008153","year":2011},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.437769Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:311a035cf2eaf5004ddce74084de6de91f97e9cd5bfda2494b0ee3f6f44775f5","observation_id":"52c013e3-e086-48dc-8197-3ef04dbfb0e5","resolution":{"observed_at":"2026-08-06T19:08:32.302912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.05542","last_updated":"2021-08-28T05:59:16Z","snapshot_observed_at":"2026-08-10T20:10:16.431097Z","submitted_at":"2021-08-12T05:32:18Z","title":"AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.05542","snapshot_observed_at":"2026-08-06T19:08:26.581979Z","title":"Ammus: A survey of transformer-based pretrained models in natural language processing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.581979Z"},"links":{"cited_paper":"/paper/2108.05542","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:2f57dc9241f4559c7c6071dc172506cc43ff4b8d05d0e00525c6cf65237e3dfc","observation_id":"3b4f26f5-2fc9-4b3f-babb-3cb84fa055d9","resolution":{"observed_at":"2026-08-06T19:08:26.581979Z","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":"2026-08-06T19:08:31.844823Z","title":"A survey on vision transformer,","venue":null,"work_id":"491a29df-7937-455f-9b6c-374a1a8cf614","year":2022},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.711966Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:1dd405054d1da222a07d61b5a3b5c95eed7a86d2ea6fe2a70f8b687ea5d9f9b2","observation_id":"bfc8332f-30ed-4919-ae56-67b4439eac56","resolution":{"observed_at":"2026-08-06T19:08:32.004526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09699","last_updated":"2021-01-24T09:38:54Z","snapshot_observed_at":"2026-08-05T19:35:00.334774Z","submitted_at":"2020-12-17T16:11:47Z","title":"A Generalization of Transformer Networks to Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09699","snapshot_observed_at":"2026-08-06T19:08:26.880163Z","title":"A generalization of transformer networks to graphs,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.880163Z"},"links":{"cited_paper":"/paper/2012.09699","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:a3548831b7bc4a100bad2fea310b8fb99e6b99559fbff8fce6e7e52290be0891","observation_id":"8e76adc8-234c-406d-8e58-fd0d8959a99b","resolution":{"observed_at":"2026-08-06T19:08:26.880163Z","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":"2026-08-06T19:08:31.570930Z","title":"Rethinking graph transformers with spectral attention,","venue":null,"work_id":"d2661f9f-dc00-46d1-922b-2cfb62c3d0f1","year":2021},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:26.992715Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:826045713724e2e05e72dfba1c495339a460a6d08b8bf6ec1297e5b9c65356e9","observation_id":"201603e2-5ad2-4235-955f-54ae67431db5","resolution":{"observed_at":"2026-08-06T19:08:31.688399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:31.154092Z","title":"Do transformers really perform badly for graph representation?","venue":null,"work_id":"47c92caa-3cc0-40d5-b031-fe44753838aa","year":2021},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.123194Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:7315214e94a4c3676b960dd13a002bdc718bf1ce38ba490381b50ef6b2e8b454","observation_id":"5b728b75-0305-420d-9622-0512dfa77b67","resolution":{"observed_at":"2026-08-06T19:08:31.344755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:30.876694Z","title":"Graph neural networks with learnable structural and positional repre- sentations,","venue":null,"work_id":"ae58480d-dbda-4f06-8693-917560cc1967","year":2022},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.210702Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:d6d2d5d4cf15cf493be36c9224302d0d39ec67c41ed9379f1c18b3b2e91fad94","observation_id":"bd342c28-d06a-4633-a941-af6ae5921273","resolution":{"observed_at":"2026-08-06T19:08:31.006859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:30.611956Z","title":"Directional graph networks,","venue":null,"work_id":"94d9e0e8-d778-4c3c-9c5d-7d91c98486f6","year":2021},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.333175Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:61d83c80bd3aaaecaa5ec047115a9a4f14e5fec611e1535b841f39c967baf431","observation_id":"b724473f-5bb7-4def-9a46-495c9aee56ca","resolution":{"observed_at":"2026-08-06T19:08:30.743767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.14522","last_updated":"2022-11-12T16:11:19Z","snapshot_observed_at":"2026-08-10T10:56:00.667216Z","submitted_at":"2021-11-29T13:27:56Z","title":"Understanding over-squashing and bottlenecks on graphs via curvature","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.14522","snapshot_observed_at":"2026-08-06T19:08:27.447922Z","title":"Understanding over-squashing and bottlenecks on graphs via curvature,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.447922Z"},"links":{"cited_paper":"/paper/2111.14522","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:925fc99e98b5c0f5cc7089b09b86301ccb9524ea2bd7a08da4991d196157147c","observation_id":"d48748c6-65ed-48f2-9920-e98d64d301bc","resolution":{"observed_at":"2026-08-06T19:08:27.447922Z","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":"2026-08-06T19:08:30.320099Z","title":"ParaGraph: Layout parasitics and device parameter prediction using graph neural networks,","venue":null,"work_id":"9c935422-835b-409b-a330-7c1ab8c023b5","year":2020},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.606198Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:21fe1b5430fa60daa19cfbd5a8a3063e2f089e8af4061a0489ad84996faa5618","observation_id":"00240ab6-b7f2-4144-b8f9-698118bd09eb","resolution":{"observed_at":"2026-08-06T19:08:30.469065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:30.115967Z","title":"Deep-learning- based pre-layout parasitic capacitance prediction on sram designs,","venue":null,"work_id":"e92a2797-da10-4595-8e73-3309994ceeba","year":2024},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.734264Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:138692bf22117334acbc48facba6d61dbcb8552ead5155258a68a8ff27aa588c","observation_id":"adca4073-814e-4baf-8869-b064ff1cc553","resolution":{"observed_at":"2026-08-06T19:08:30.227509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:29.795081Z","title":"Rethinking attention with performers,","venue":null,"work_id":"8cf02c22-a879-43c5-a17b-383092e13740","year":2020},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.802221Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:4fecdbb901a873630ac09213f8252b62be073ee7a63cb2fc287872d539c3e119","observation_id":"4a11af24-e02e-48cb-9ecd-596154e48acb","resolution":{"observed_at":"2026-08-06T19:08:29.972548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.00982","last_updated":"2022-12-28T04:57:24Z","snapshot_observed_at":"2026-08-10T00:17:51.124629Z","submitted_at":"2020-03-02T15:58:46Z","title":"Benchmarking Graph Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.00982","snapshot_observed_at":"2026-08-06T19:08:27.863764Z","title":"Benchmarking graph neural networks,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.863764Z"},"links":{"cited_paper":"/paper/2003.00982","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:c53f9461ee04c8546667b1b4f0297f9a3e8cee27213fcec943e69eb3b201db70","observation_id":"4538745d-77c0-48ba-aa57-2972ecc5c03c","resolution":{"observed_at":"2026-08-06T19:08:27.863764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14202","last_updated":"2024-08-22T23:22:33Z","snapshot_observed_at":"2026-08-11T00:51:33.457880Z","submitted_at":"2024-02-22T01:07:48Z","title":"Comparing Graph Transformers via Positional Encodings","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14202","snapshot_observed_at":"2026-08-06T19:08:27.974394Z","title":"Com- paring graph transformers via positional encodings,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:27.974394Z"},"links":{"cited_paper":"/paper/2402.14202","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:b1671c696740801ae0b7a37cca7093812b0f3b5e45210b98272e8fdc1c75d8ac","observation_id":"6c12d182-7311-4ef4-ac41-e8afaaad211e","resolution":{"observed_at":"2026-08-06T19:08:27.974394Z","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":"2026-08-06T19:08:29.595372Z","title":"TS cache: A fast cache with timing-speculation mechanism under low supply voltages,","venue":null,"work_id":"32c3dbb5-5ff0-4b4e-bb9b-07ea53bf25fe","year":2019},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.040371Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:7b9124091178de78372f082691ec7900ccf4dd81474e45ed5d79eb94b461afeb","observation_id":"2f453e90-8cff-4b7c-8b00-8f289bdabda7","resolution":{"observed_at":"2026-08-06T19:08:29.697164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:29.309812Z","title":"Structural information enhanced graph representation for link prediction,","venue":null,"work_id":"b6d42737-2040-4892-8289-d30bb56db1c6","year":2024},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.103950Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:ee3f4d283c7b93132ef3ba04124ff24bd8609eb0119682b230ffc11615f6a6ab","observation_id":"8e25c259-e5ca-4092-bc11-7c7eeda6198a","resolution":{"observed_at":"2026-08-06T19:08:29.437275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.07553","last_updated":"2018-04-24T08:19:32Z","snapshot_observed_at":"2026-08-12T14:23:30.636240Z","submitted_at":"2017-11-20T21:28:40Z","title":"Residual Gated Graph ConvNets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.07553","snapshot_observed_at":"2026-08-06T19:08:28.171038Z","title":"Residual gated graph convnets,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.171038Z"},"links":{"cited_paper":"/paper/1711.07553","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:dc6f5baf58e0733539c02af6d09be2f288868953487b8c8d47e7fc606efb1dfa","observation_id":"aacc6892-2845-49af-ad96-e9aa4717a3a2","resolution":{"observed_at":"2026-08-06T19:08:28.171038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08993","last_updated":"2024-10-28T09:03:11Z","snapshot_observed_at":"2026-08-09T14:15:43.991975Z","submitted_at":"2024-06-13T10:53:33Z","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08993","snapshot_observed_at":"2026-08-06T19:08:28.244306Z","title":"Classic gnns are strong base- lines: Reassessing gnns for node classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.244306Z"},"links":{"cited_paper":"/paper/2406.08993","citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:a271329c0d27ce6856aa377075a14acecd1b36486c0073766abd5a317d706ecf","observation_id":"7e830c07-6ab4-4b01-a888-c793b861daef","resolution":{"observed_at":"2026-08-06T19:08:28.244306Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:08:28.310234Z","title":"Fast graph representation learning with PyTorch Geometric,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.310234Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:ad503828bc86fc1dd1c72fbdc9d7c37a8570a5ef8bd8c7dc795b1bfd37fe4b71","observation_id":"f4f366a1-cc61-413b-9a37-9ebd466bc22c","resolution":{"observed_at":"2026-08-06T19:08:28.310234Z","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":"2026-08-06T19:08:28.984091Z","title":"Design space for graph neural networks,","venue":null,"work_id":"97fea211-0335-4c1d-8477-1a872fe59d30","year":2020},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.374643Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:9a5e79c4b348eeb558562f07d74c8377426142073ec7fb8cd8411c5ca277b100","observation_id":"6e7ed161-9ec5-4db5-b4b3-c83f5653cd1c","resolution":{"observed_at":"2026-08-06T19:08:29.145681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:28.771862Z","title":"Ultra8t: A sub-threshold 8t sram with leakage detection,","venue":null,"work_id":"2321069f-1012-40f1-825e-905f3eb56fdd","year":2024},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.458677Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:a840019240f61604887bda7f08bd1037728147813c5b5b387193374ec4dba917","observation_id":"4f35143f-8955-4fb9-b3d3-7695e99120c4","resolution":{"observed_at":"2026-08-06T19:08:28.846352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-06T19:08:28.653963Z","title":"24.4 sandwich-RAM: An energy-efficient in-memory BWN architecture with pulse-width modulation,","venue":null,"work_id":"5b76e8f3-8faa-4683-90f4-92b00d3af86d","year":2019},"citing_paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:08:28.521321Z"},"links":{"citing_paper":"/paper/2507.06538"},"observation_digest":"sha256:f385042657d8557097825d7d59150ddd723e4d0f59ad17890ef65806c8fa9c2d","observation_id":"55bc8552-9bfb-4cb6-863c-669211bf1dd5","resolution":{"observed_at":"2026-08-06T19:08:28.707354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.06538","last_updated":"2025-07-09T04:42:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T01:08:11.470862Z","submitted_at":"2025-07-09T04:42:18Z","title":"Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":30},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.06538."}