{"as_of":"2026-08-07T09:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6bf2df5a946fa1073a22f3c1bb80be741bc408dacd04e9edf583bfdfea277304","coverage":[{"denominator":94,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":94,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T10:08:12.401078Z","state":"measured"},{"denominator":94,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":94,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2604.04290/citation-record","integrity":"/paper/2604.04290/integrity","json":"/paper/2604.04290/citation-record.json","paper":"/paper/2604.04290"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:2e78bac8bdf5bbbb9ea20ff8c7210001e94afd87bd99b1bfc2504f60c0c5ebc8","observation_id":"f2838735-e088-4f25-906e-94f6a158d83e","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:9b47a0bd88770005ed173677f427ec42d23189f832b444dae07a3e6243631912","observation_id":"19ed6403-b0f3-4ad9-837c-59fdda87e6ce","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Hence� θ[a(θ)] =C•0 and centring removes exactly thek= 0 contribution","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:91ae655e8459a4d72ece6dde5c78e7c07047df220103b6034bf9d861f878aa18","observation_id":"7ec807dd-fa62-4193-a044-b241a45772af","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:e110c3f0c5f8c8a912963a4ead8b092a74378b72e26f8192bf3f81030841a5b3","observation_id":"2c565463-cf2d-49e6-b271-615a92b719af","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:dc8fd4086cbfa8d06912c497f62412131829f2be9b269c12188165795cbe7b8a","observation_id":"a7266c87-b4d2-42b5-89a5-9c6238865285","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"The accessible spectrum and its construction via difference sets and Minkowski sums is standard in the QFM literature [14, 15, 19, 20]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:cabb7cea3c14b06a1ae3098d3a02a0b1ab148fe8d7c653357b57e2771111c6a5","observation_id":"97b01092-dd69-49ee-b151-0ea95ffd9905","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"From (43), Cov[a] ωµis large when the rowsC ω,•andC µ,•have aligned phases on a shared set of centred harmonics","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a89364203b0c31ffdb6bf912dfaf70fab4a20f7c2f959a2a4fcdf00d83afc2bb","observation_id":"4334af5b-8ac5-4cbc-8774-898db3266cbb","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"(67) This isolates a universal torus objectM(θ) (depending only on the character map and the parameter space manifold structure) from the architecture-dependent mapping encoded byC","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:26cebaeca6fde261dad93900dc6ed20d76c299f91a2384de0596c2f789aea218","observation_id":"f776e055-f255-4a7a-8a45-8a47b22bd2c6","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:fe589df85127a3f71538396db92f77a17c9f550107d6c589e1cc4df08e974e1c","observation_id":"ce6afcff-04cf-4e88-a662-49a1012305d5","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:48328f8b04d6af23638486740c074ad2e1dda784d75303fa030df35defe7a45b","observation_id":"f2e2742c-cc30-4571-8365-9a4756dcc12f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Throughout, the trainable blockW ℓis taken to be a depth-drepetition of a fixed ans¨ atz pattern as defined in [10] and also used in [21]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:8904951fdb78d83313789cdaf87e3af6c87ddd2ac6268d3212e0a5420f2d71d5","observation_id":"fd43fb83-5031-4c54-bcf4-e77722578270","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"For our circuits where we re-encode on each qubit and each layer,ω max =nL","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:6f02fc697e71d2b6ec4fa3a47bc3cd69a7e3acb11dd3be216e0f0aca95a5b7b9","observation_id":"968c3bc5-f3de-43ec-b149-6bb89d5eecd0","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:8aff352b84ae0b6ede258cc073db0562bd43fa5aeecd4af6a62a9b05bbe15acb","observation_id":"28776c34-f6f3-4028-a377-202bf80be8fc","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:600e4632ce03a9078d5163725c87984a3e90daf631523399ee71f41d3ba70ff3","observation_id":"eff687a2-2f83-4e44-b75f-d62b07d0181c","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:5dc204ef1a34822478138f6a524dc3062d0a482a5d7a17467b7b5e7c59d5d90e","observation_id":"0903a84a-640b-4a7c-9373-8bec88c06613","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:434d6e3d8a5d7b81d9b0eecca8e718d20bd37011fbee364fe70fbe8722ea54f6","observation_id":"eb1e31d4-0b9f-4bfa-bdba-1d9876bed370","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:5abd23571e0e2c45c35f7ba6c3fa7606f09e644985bb3afe2d3e4ec2433ba33a","observation_id":"4a9297a1-9cb8-481d-89d4-01f9dd5669be","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:816adcb8533b329e4dec942f7b3807a3f1ab018efdf659c1fb83f8db719b0fce","observation_id":"c3259944-7bdc-4321-be33-9557304bcc50","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:acc548b47671194c79ebe1eed064dd8e0b5334634db1da55eff348dc5e470ebc","observation_id":"d7ff40e7-cc70-4552-bf16-62fb516f13e2","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"As in the correlation-matrix comparisons of Figures 4–6, we report both normalised Frobenius errors and cosine similarities","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3716a6c5eb50bc1f174f724e05e92de971722eb506af78a5989adb41d29c83fd","observation_id":"8520d5c2-49a3-4459-bda1-088237ce0345","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Architectures employing non-commuting feature maps or qualitatively different data-loading schemes may not admit the same finite harmonic description without modification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:e8c2afb27b1776fb4e1c47d766dc230038acf1a0a9c770511e8ad8250676d092","observation_id":"81e0b0bf-acbb-4447-98c1-791634fd62bb","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:fffcf6eb271251b9deffb98da1dcdaee0376788a4755539093c113399f8e2372","observation_id":"6196d6ed-012a-43dc-82ee-fcf916bdfce5","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3f9bfe3edb910c92eaa253d0d318234adedc9ec0fb77050256ae1fd22191e3dd","observation_id":"6d18d366-8e5b-4318-aa87-bc8bbcf79386","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:612c3db595abb59375b0b59aa77f881e31b6ac7793b89df769c921d6bcde22a7","observation_id":"c0349bfc-a923-4a8a-a9cb-b4767f76fafa","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Concretely, second-order quantities control the linearised training dynamics around a parameter point (e.g","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:ed50dbc8bb10eff0b3b8838dbd24c12ce4edbfdb58d15adcdd48de6b562f4f93","observation_id":"50b07008-e20e-438e-ba88-78d868f5dca5","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"sampling, conditioning, and lattice effects)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:118e8ec0c4c03c18a9eeebc4f2707a0213353f97550ecb38c54e184b485df284","observation_id":"7a5066ca-8e3f-4dc4-9c29-93973bb941a1","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:35491ec95240ba11c4cefcd3de840f52d434d49750352ca4e53f8d47ae118748","observation_id":"734bb842-35d9-4814-9c99-13609ea9e8d0","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:029c66fc039a1c7e4e6c9cff0b54b07813ac390f41e5e9a944ed6937bfb25297","observation_id":"fd3eb08c-f10b-494c-8af8-a997cb9c39f5","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:546653cac40ddf7e6492dfdeafaee9444ee3f97643f5ed8b2ac5e856ee38bf49","observation_id":"0314c1c3-625b-483d-9b2f-1f2fb84b3355","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"The dependence of data-space kernels on the input design viaVinK(θ) =VH(θ)V †, is therefore a relevant consideration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:28962bec1bfbf550830de925563a3d5ed200b696993184563fe3c66d01d0257d","observation_id":"3df2e2b3-46cf-4bb0-8925-2490016227ab","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:7c060793e2059ab3de847e75cb231f8a47aacd3cb2416d1963d0565caa3e998f","observation_id":"10f235c1-8002-42df-8bfd-3a333618038d","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:4ce4f41dd82c11b7671b80340fbc33490c24feecfaf26ae6017cbcce613a0e0a","observation_id":"aaeacc23-c867-42e2-8ef9-94c8c12e574c","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"As noted in the limitations, this focus leaves open the systematic role of higher-order statistics","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:669897987094735f47b68c629bb2189ef5af44b7dd940e4343a655c873156465","observation_id":"38fd1414-4d7e-483c-b1b5-7522a247a170","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"conservation laws, permutation invariances, and problem-specific equivariances)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:ae19f121c72656fbbbf6ceab51b2c136ffdea883bc3a55226d5573349a5afe97","observation_id":"9f6836ab-1ab5-4bb1-8b4d-36e62ce16df1","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:1da44cb1377a5946a746bc3d8099546bfdfec6d5b8944d7185267fbeab1e2cae","observation_id":"9a507337-9d32-449e-8a8f-0520f1679e39","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Quantum machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a7aa26a0eec474799eb36c1f135d22f1ba222c5a9b3936e0ae1a67f810961b9d","observation_id":"4b42c072-cb50-453b-aa92-e6be918d9107","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Schuld and F","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:19ea7bb57e0244b18b5602bafbba6ada0ef3c5c0350e53453820ae220848543d","observation_id":"8e90a470-dde4-467e-84c0-354d43eda98c","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Variational quantum algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:d31dbc08d0aac4c7040415571bb5d782198ac76c278a2813057095ab9a6d22be","observation_id":"2fbae0ec-0087-4bc4-abd9-612de779c21b","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Supervised learning with quantum-enhanced feature spaces,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:81fb372f5151d076ef4aca7bfb43628f35236e560ac6479512a3d878d64f22e3","observation_id":"52a54052-cf6e-4c16-b259-b053899b4eea","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Evaluating analytic gradients on quantum hardware,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:06a698b6ef9391c65d9ed64663dbc13d02252357dff7c081e9cfc83334e0dc54","observation_id":"853e1f44-1d32-4995-a01c-59e600550773","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Barren plateaus in quantum neural network training landscapes,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:ca174ac54a04c27ea97a7d2390f06141bcffd6aae1da08d9cecdf3f2a8e9bd1e","observation_id":"6f5ef3b7-fe9e-41dc-b3e4-cf881ad7625f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"A Review of Barren Plateaus in Variational Quantum Computing,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:0a34c4513706dad979014ad2ab42585439376d9f6c8efd8039a2d7e73cf7897a","observation_id":"ee040806-7a70-4b88-8042-7061b196d9d7","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00781","last_updated":"2025-05-08T17:14:28Z","snapshot_observed_at":"2026-08-02T10:17:34.758853Z","submitted_at":"2024-05-01T18:00:10Z","title":"Barren Plateaus in Variational Quantum Computing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00781","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2405.00781","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:2eebbfb0f02271d8711e3ff35203b53190e9eb699303666e2bcaa5250ea14564","observation_id":"0a7cbe80-c9de-4ec3-ab32-befaaf2f1835","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.09342","last_updated":"2024-09-03T14:56:34Z","snapshot_observed_at":"2026-07-06T16:19:37.701195Z","submitted_at":"2023-09-17T18:14:10Z","title":"A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.09342","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2309.09342","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:2aee6143ced12dc95f0c6cc15448880011713f7bc2c6f8a55139800263c5093c","observation_id":"7b665bb2-f927-4251-87d6-5cdec897f31f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Noise-induced barren plateaus in variational quantum algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:c1ae0e9a7be49c96bd2608bc64d46c2fcb131c46e5b3fe8c7d581ab4b57abc5a","observation_id":"a763ba91-f3e5-4d6c-83d5-45ce967cee4d","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:76f39cf325ad51d40ac0c1b41c786233e7635b897a647da1d4e4de8bcc24a1f7","observation_id":"0a74fc1d-0b27-44f4-953f-55e19135ba00","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.11676","last_updated":"2021-09-23T22:39:48Z","snapshot_observed_at":"2026-07-06T11:50:50.694694Z","submitted_at":"2021-09-23T22:39:48Z","title":"Theory of overparametrization in quantum neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.11676","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Theory of overparametrization in quantum neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2109.11676","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:449e3593d6e1d52455d7e435712685268d06d2c08950a97747c07e9931fa7dc9","observation_id":"bf1fde90-d97e-4be6-80ee-16cbb1984d5f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Representation Learning via Quantum Neural Tangent Kernels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:579abccc293f922fd45960ad13f0765736156993335c6bfba368cbb01ba57b7f","observation_id":"c0548b1e-1b6b-4e59-af45-2363539b03f9","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08232","last_updated":"2023-04-21T02:43:33Z","snapshot_observed_at":"2026-07-06T12:38:30.234691Z","submitted_at":"2022-02-16T18:24:37Z","title":"Quantum Lazy Training","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.08232","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Quantum Lazy Training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2202.08232","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:f3c1ad0ddf46974510864a8a0b4e91570c5d1182d7da3785be2a2df6930a32c9","observation_id":"8724d5b6-b96d-4e54-a8f2-84d8e6aece7b","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02085","last_updated":"2020-06-04T14:01:23Z","snapshot_observed_at":"2026-07-06T08:04:56.737336Z","submitted_at":"2019-07-03T18:00:50Z","title":"Data re-uploading for a universal quantum classifier","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02085","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Data re-uploading for a universal quantum classifier,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/1907.02085","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:1a4c30b0b1c032642fd360ffe95a47f4b72cecf80545b021a14d1a73cbe9f948","observation_id":"657979f6-f003-40e8-bff3-4d33f3cfccf7","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Effect of data encoding on the expressive power of variational quantum-machine-learning models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:4c023caf43a375098a728fefa16025af345131142a70d410771d6bfc4b01cd8e","observation_id":"b812539b-1cef-4618-9357-3a06fd19e5a0","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/9807006","last_updated":"1998-07-01T19:34:39Z","snapshot_observed_at":"2026-07-07T07:27:58.465159Z","submitted_at":"1998-07-01T19:34:39Z","title":"The Heisenberg Representation of Quantum Computers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/9807006","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"The Heisenberg Representation of Quantum Computers,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/quant-ph/9807006","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:615d2a858892772773e75019eed40dd2fe972adaa9b5618c04da5d6d43335e33","observation_id":"fa407b03-049e-4271-804d-3f211f4b7379","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/0406196","last_updated":"2008-06-18T19:36:18Z","snapshot_observed_at":"2026-07-30T18:49:59.479815Z","submitted_at":"2004-06-25T17:57:42Z","title":"Improved Simulation of Stabilizer Circuits","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/0406196","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Improved Simulation of Stabilizer Circuits,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/quant-ph/0406196","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:c488b7c19b95874748b6d3c6c7390aa65365d76a0e1e40edfe570a2dac9e7d07","observation_id":"b4e13b47-cee7-4b11-8afd-f0912d60c4ab","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Simulation of qubit quantum circuits via Pauli propagation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:553126941f7480e88e5e425a957283854f9af915aa696490472b06746487806f","observation_id":"75791575-41a6-44d9-ad60-a48a067088d3","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Multidimensional Fourier series with quantum circuits,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:9cbf1b81d773f05bc76da9bded84860b4801b9118c9076df51e2f2f72f18ca4a","observation_id":"feb08f9b-33ab-4f80-9067-27f4e41ffceb","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09417","last_updated":"2025-09-02T09:12:37Z","snapshot_observed_at":"2026-07-06T17:44:37.894023Z","submitted_at":"2024-03-14T14:05:24Z","title":"Constrained and Vanishing Expressivity of Quantum Fourier Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09417","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Constrained and Vanishing Expressivity of Quantum Fourier Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2403.09417","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:67b53af26e72e67c454bc0e25a2587108841d4986739c08669b2212c251d3519","observation_id":"f5243773-232f-4e73-b6d2-30cec92f4bf9","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.20868","last_updated":"2025-08-28T15:00:37Z","snapshot_observed_at":"2026-08-05T14:50:31.228946Z","submitted_at":"2025-08-28T15:00:37Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.20868","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Fourier Fingerprints of Ansatzes in Quantum Machine Learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2508.20868","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3176076bfa4afbf95498e6044a95029fde88adf8a3e88dc3a99c042e8618aae7","observation_id":"647c495c-936c-443e-9692-944ff05ab014","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.03450","last_updated":"2024-11-07T08:10:41Z","snapshot_observed_at":"2026-08-05T08:04:49.609366Z","submitted_at":"2024-11-05T19:07:26Z","title":"Fourier Analysis of Variational Quantum Circuits for Supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.03450","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Fourier Analysis of Variational Quantum Circuits for Supervised Learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2411.03450","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:174e84c6585b176409826bda06d09dc3a360282dc44da326d3f0a138410f357c","observation_id":"2f72aaee-7415-4911-9983-f1e9bf6845ee","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Spectral Bias in Variational Quantum Machine Learning,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:2e41f9db7f6b28a006947f0599c5c979378712a0fcf25da5774737e3c55aa23b","observation_id":"03a8af79-647f-46a0-9edc-1f6b8955ef9c","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Quantum tangent kernel,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a747e6bb352191dc45ef8140ec41008af13e4bd43e2d583ad62a67c8c1e3ea38","observation_id":"af7a0939-b62d-4e06-a17f-985ecb78573f","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Katznelson,An Introduction to Harmonic Analysis","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:48a49ae1bb4b091e562ed92ca29347039bb462270664991cd47d53a1c8608cf6","observation_id":"3cdb561e-4827-4e4c-941a-2711336c569b","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:da4068f2630f671b2c122d49a5056b1feb46e68b4e288476559886763bc95054","observation_id":"d60c8ae2-a6c4-48e9-8583-9ee03fed7823","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Neural tangent kernel: Convergence and generalization in neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:7fe25576e68c853e1cce0e702232dafdf1ceef683d06c3e35d5ac40eebd2b501","observation_id":"faa3e1cb-47f9-462a-b379-2d2607b30cb9","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:7c39d442e1a358646ec895d02da65773d51a7ee702ff1f84ef7d81338a772108","observation_id":"1af02cf9-ae57-4803-af19-39aeb82583c7","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Random Features for Large-Scale Kernel Machines,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:b2d067ed6b675dab535cc487ff10a34727894d39397b568d4d131521d5635faa","observation_id":"54565f63-6822-44fa-a7ba-67a4823513a7","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"On the Spectral Bias of Neural Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:1eb2f445ffb0ff0a3090276f4545669ecaf3c6cff1df08eab4d9e4781bef75a8","observation_id":"e280e14a-706c-4e91-95a5-d412ce991781","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"The Born Ultimatum: Conditions for Classical Surrogation of Quantum Generative Models with Correlators,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a3352ff2c3083c760b83fd28efe17435ee0be654e098e47e01c06a9829af9c25","observation_id":"b4a9adea-8cd0-47c1-a5f1-9a3bcbed6596","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:9789674000120b7a80597a33c4eb66d25a68cbdfad6857a47d9c8ee519e85bf6","observation_id":"32efb6c5-9e16-4bd6-9902-e047dee46ed3","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11901","last_updated":"2025-02-06T16:55:16Z","snapshot_observed_at":"2026-08-05T10:51:52.349189Z","submitted_at":"2024-08-21T18:00:08Z","title":"A Unified Theory of Quantum Neural Network Loss Landscapes","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11901","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"A Unified Theory of Quantum Neural Network Loss Landscapes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/2408.11901","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:fb8576735623d32134d25d67996525f8a2d0571b283a9a5f3906709f8fe4343b","observation_id":"ecbbda33-3255-4b9d-bc04-056b09a775f3","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:1d6e10ddfc11e7a9a20aed11644815a0097a7e9560a6f8cc40caac75e01029be","observation_id":"afe400a3-06b8-44de-8314-5e01f2543636","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/9705052","last_updated":"1997-05-28T23:51:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"1997-05-28T23:51:29Z","title":"Stabilizer Codes and Quantum Error Correction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/9705052","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"Stabilizer Codes and Quantum Error Correction,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/quant-ph/9705052","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:7778d17861d2c9c5e1f4be984c54e7534f1c5cf29ef40b0ddcaf5768c3cf9bca","observation_id":"dbd6b9f2-b48c-4e43-8a11-3b53efbb3695","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/0304125","last_updated":"2003-04-18T09:21:19Z","snapshot_observed_at":"2026-07-07T07:12:18.544845Z","submitted_at":"2003-04-18T09:21:19Z","title":"The Clifford group, stabilizer states, and linear and quadratic operations over GF(2)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/0304125","snapshot_observed_at":"2026-07-13T10:08:12.401078Z","title":"The Clifford group, stabilizer states, and linear and quadratic operations over GF(2),","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"cited_paper":"/paper/quant-ph/0304125","citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:9977f1329e5bfdeb782d9627ef0359af84c799735e0e7c8c46c06c45e9ca1606","observation_id":"c3ecd38d-da83-4d2c-b248-5845da46da34","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:321219f1493f0fc7c08dfdc79d293eead578b6b5b81a7f89f10bc40e935b013e","observation_id":"08af2645-3aa6-4e59-9467-49f29e730bf2","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Quantum Fourier models and encoder-accessible harmonics 31","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:2386b8cd4f140bfbb1c806d0ca17284cfcb7d8903575bb2560a471ea439ddd01","observation_id":"23479427-df72-42a1-b995-782d4a4d6d25","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:e29a63e99963f018c9a0034755759633adcf7a3df07b38d12fc85e509d391e6b","observation_id":"9ab07353-03ab-4493-86a1-4e53cb025fe1","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:b3963da5ce0fe9cdec0947f9abd134e00750bb34ca4a89014c74bd9d1d129c44","observation_id":"e641d2df-0c31-4e50-9d7c-acb1aa5774fb","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:4db632bf3243f29b2ce70bcdab323017a59ecf32b4bbd829d9a7738bdc55082f","observation_id":"9fea2ff0-8814-44ec-b68e-6163bade780a","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:73191d4b682fd471adb7f24cb2d0ace269e711d0c68882eb753630abbd02501d","observation_id":"94f41d70-d166-4cda-9cd6-4277d4584784","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Proofs for coefficient statistics 33","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a6e23edc9c51efeeae7b8db261ead0889dc23d8788bd936a4a200d00b962e8cd","observation_id":"ef485af3-673d-4959-a7d0-122573e12881","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:68648759dbd085a6ac5d706b8df94bde72ec0992e47a0057ef34733a6ef88f63","observation_id":"36a5d6b2-8485-4a45-b888-323ac7a60a38","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Pauli propagation and node expansion 34","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:f38f03a9bc5818d134c84f7fdb360e57513063669fb3c5c45706dda9d8e264f6","observation_id":"4538b034-4d1d-405a-9144-826605567c9a","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a44da6af05c5922bfb71c8e20e9ae80ddda1d2e4cde07233f0457ce2abe8dc21","observation_id":"bf9fcef9-15d5-42cc-92e0-0d2e664b4a95","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:db8b9950b8e31a7b5f0f04d2e9072be625005771b78b25e522bb0e706e546ac5","observation_id":"ccc57732-9fe4-4749-81d1-dbc9cebf1d72","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:15b2404dc79bd9191949995c14c397efba3c7cba99a277bb7de860e2755db2e6","observation_id":"77e10f9b-fdfb-4550-b3ee-919dd306dd90","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:ae9d69360aff12898cf83d0706f6b3ba2a84f424f923ce8a1a05a20e2b23b42d","observation_id":"bc000def-675c-44b7-98e1-3e33ab12a694","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:c19e264c661f2b65eb26995d6ec4b60b84f2e4674b64eebafd0afe97783eb320","observation_id":"ad84a789-f683-4812-b6ae-cb9b2475ce3e","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:0004598394b826911164e6ae4f510e15727d698d0d20999d8bd728c8405e0f97","observation_id":"d17fd534-3f8c-4de1-8c8b-8ddd8f707d98","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"��� � Trainable parameter vector on the�-torus;�is the number of trainable parameters","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3353f374646b53dc8d86dc353d8b05f7dbadf7e2d0452a9537612166983b9bb6","observation_id":"3464b2ea-99ba-4679-8854-a797b36eb90b","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Note the similarity in structure to the circuit’s correlation matrix in Figure 4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:ebc3af9160c67c97108b4d966aaa391139f4b337c07680c09476a25cfd09e528","observation_id":"f633f0fb-73a3-44a2-95e5-9fc9d2d20587","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:5d12b99df73270d0d7c66d50b4fdf7c386b528238f034a2064ae7a9445f52809","observation_id":"605a0e21-0194-40a9-8a4e-21e08d4f91a0","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"����� ���(Difference-frequency expansion)�LetS(x) = exp(�ix�G)with commuting Hermitian generators G= (G 1,...,Gd)and joint eigenbasis��λj��j","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:3f757acad027e005a2a7bdca18588794aaf5d6a1354be6b6aaf351c1f271a8c2","observation_id":"dab234ae-b73e-4235-a1f2-29893dca69b6","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:a215d5be28fbb37d8906e8df79695a047223e209f30fd1b4b73c66b98ae2ee9e","observation_id":"23cc04fa-81e6-4e8d-a53a-f252858f76df","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"��������� ���(Path set and redundancy)�Let Ω (ℓ)be the difference set of layerℓ","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:c1abb0b9bed4728548d2d66bac0a5266f52fc58862ab3f7becef8f22ad6887a5","observation_id":"4a3e0ccd-8d63-4fb2-bd25-69969334bd05","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","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":"2026-07-13T10:08:12.401078Z","title":"Single-qubit Pauli encoder.LetS(x) =e −ixZ/2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-07-13T10:08:12.401078Z"},"links":{"citing_paper":"/paper/2604.04290"},"observation_digest":"sha256:5c004b0ef52105ff110e3b04aa4b4a00ba3c258dced473d94eb3e6e9b70a2cc5","observation_id":"dddfabc8-d7fa-410b-aa36-78c56a613dce","resolution":{"observed_at":"2026-07-13T10:08:12.401078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2604.04290","last_updated":"2026-04-05T22:13:00Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T05:07:14.477387Z","submitted_at":"2026-04-05T22:13:00Z","title":"DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis"},"reference_resolution":{"displayed":94,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":94,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":94},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 0 inbound Pith citation observations for arXiv:2604.04290."}