{"as_of":"2026-08-10T03:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1bcd69c10ad34b4f399ed5e3256bf0fbd48220e0c4bd2bf1778817e4a1107ac0","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T03:34:15.326354Z","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":"sha256:1b2a661f3f13d138ffa244f819e5034236aeb6afd8e04ad701739a966e913367","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":"2026-08-08T03:34:15.326354Z","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:f075e39cfbc345aa207a792f64955f54e2c46a5468054fd2911b81f565193725","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":"2026-08-08T03:34:15.326354Z","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":"sha256:7728b5a2df0654886231a119fbaec6fbbf4cf5e79ad4f73e1141eae8af809c36","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":"2026-08-08T03:34:15.326354Z","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:a19ee49dee4db5bdfa1cbbf646c47d63335a40bd74b2bf0c01770f1345f318a2","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":"2026-08-08T03:34:15.326354Z","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:e86f96fd5c0a82040242a135a9446c71d579398fef4235763baecf1d69cd940d","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":"2026-08-08T03:34:15.326354Z","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:ad988cb0e431dbb4ac71b420091acabda81d716beb1d7ca94fd2d6ac5f519494","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":"2026-08-08T03:34:15.326354Z","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:e7f7325b7107634c5d65e25e7692b6373fede3417d79b4153ec91e68f0d3f6b3","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":"2026-08-08T03:34:15.326354Z","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:ab99ee3e2cf5012cf447aed8b8325c37e890f3fd35560bacdb952bcbfffff780","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":"2026-08-08T03:34:15.326354Z","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":"sha256:6f85efe731b419b4b54c8effbe4fac50a2230adc99b5ed30c33bd10e6ac81d0b","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":"2026-08-08T03:34:15.326354Z","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:dfd9a065e2e1dda85c0139805eb868bde108d46bcfd3c4bc2fcbeba779e85899","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":"2026-08-08T03:34:15.326354Z","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:f40466ade7d4e98bf95a49a081fb42ba81820d0433e0bad151aff34bd6f0b036","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":"2026-08-08T03:34:15.326354Z","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":"sha256:00918df2ee0540cdad7561d31eacebedaf33ce0f9cb194d5b928b0632a2bffd0","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":"2026-08-08T03:34:15.326354Z","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":"sha256:99bd5be2cac994df9a807218c459130ce7cbc2e2ac3c4d2ffc91882993dec2a2","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":"2026-08-08T03:34:15.326354Z","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":"sha256:0463f796bb7dccc76333047c3fdf59157115e97f58555743122bd2e83ddf60bd","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":"2026-08-08T03:34:15.326354Z","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":"sha256:3e924caa40fb5f7937814fc2da8fe2061221ac9c12ff6a4c7470c62d03dbb3a5","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":"2026-08-08T03:34:15.326354Z","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":"sha256:720b34afc23ac2bc2a02f28b9257cefa5cc38e1ec3fadbd35d948caebf61d8f3","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2d522a269eb3af0ec5f7010ad110c3a7a46dd9165a330000bb246b09d39d9e4a","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":"2026-08-08T03:34:15.326354Z","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":"sha256:467eff26d3394a0978ef49ec4a4dfd9ff6567c42cb91dfa22d07665b3e9e31ff","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":"2026-08-08T03:34:15.326354Z","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":"sha256:5aa423c2185068aa37addc8cbe2e670ccc570dbb3f206847c907c0a879ead00c","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":"2026-08-08T03:34:15.326354Z","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:fe0df330fe1b89222c929319e6ae12e72da0070583be3cfe116381d3fe706a68","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":"2026-08-08T03:34:15.326354Z","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:ab60a5fbd150f78e9b8591c29678b6385c86aa2574085b80bd413fb515b9ae92","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2a518393918e7f348ee4bddfbb1d22cf2b13b675917a5bacb425b7a2bf149c03","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":"2026-08-08T03:34:15.326354Z","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:e030a2726969977dd996661b087759b72a0d0510ed4ba030315b7e086ca0852e","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":"2026-08-08T03:34:15.326354Z","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:ae2f264e33851219e38d7faac1730d45ddea41a048241f9f078fac3510cbb890","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":"2026-08-08T03:34:15.326354Z","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":"sha256:7dbba6c9847d9935a765a6a850b0e4b0afe0451faf1bcab5615c0b2d68c68027","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":"2026-08-08T03:34:15.326354Z","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":"sha256:45b0301c3ca9c7224bbc53e89ba39b2e51a7b4888be81046d6ed0267bb85fd18","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":"2026-08-08T03:34:15.326354Z","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":"sha256:87c345d4bf76b67e1d53106b796b2b6fa11b5afb5153d3deca14c246542e51d5","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":"2026-08-08T03:34:15.326354Z","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":"sha256:683afdcff22d1473c8256906e5eeb037286ced595f3e04e1677ef46d40da819d","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":"2026-08-08T03:34:15.326354Z","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:e30790a792b71648c146ce752156d232a456c42470dce2412c6fd3c2060bad9c","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":"2026-08-08T03:34:15.326354Z","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":"sha256:67f321fbedd1500da227d5f6a389c19ffe40ae8526d5dee6fcf84b4d808025e6","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8237c95a2b09b66bea3f7945f192138d1c9662447604b0b7f8db3d56105d8f34","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":"2026-08-08T03:34:15.326354Z","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":"sha256:5a162f02ed7829a3e0c789719056e10a8c99383196fba954c7c47e0a3c43ece1","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":"2026-08-08T03:34:15.326354Z","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":"sha256:1ed7f832e5353578d49f43860c3981a21ad5135f8a042479711a3e8b7ff157ab","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":"2026-08-08T03:34:15.326354Z","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:ab8df832fa1880f25ae51d16b3fff43104eef09f8277ddb41a101a32e2d87897","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":"2026-08-08T03:34:15.326354Z","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":"sha256:95d56987d62b2e5cda2e08cadb87aac199f37815ab5bac8b4cfe2e752e900fc8","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":"2026-08-08T03:34:15.326354Z","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":"sha256:0557479f1880b4c8dfe77e1df6c5aa6143e19a958f196177b66e1493cdb8daf5","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":"2026-08-08T03:34:15.326354Z","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":"sha256:03a5ef2ad6178b3d76a6db16f57b10e6086461e62da34aea4d6cbf6028dfb6a5","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":"2026-08-08T03:34:15.326354Z","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:e2c50f34dd6045ac2a4e7e3311b55a43787a2c077da611831c1970cc103b3072","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2df63df2e12e0cb326caabe1bfa70de94a0e7d3ae26f55266a4cc696b6144b65","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2e5ffa800be9e3b9247308735446c23107250918c0cedfb4a561fc4c7374a1a3","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":"2026-08-08T03:34:15.326354Z","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:e22bf0f3ba25f32f1be7a5a2484d28e721ecd1a584c79eaa7eef1612d09b9a2f","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":"2026-08-08T03:34:15.326354Z","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:e3184dde96b94f4f5e1951522fb3aaa5ea2eba7205698e9370bd1176870d1a81","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":"2026-08-07T23:00:28.134283Z","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":"2026-08-08T03:34:15.326354Z","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":"sha256:5cb5769400ffa6cd661e150e370930926091f56a44c70fec3c75cc61a96bb3fc","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":"2026-08-08T03:34:15.326354Z","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:e7a99bed231a263d80c1ac8498b71e45606079f5f1d9460708b63bbccadd03e3","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":"2026-08-08T03:34:15.326354Z","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:fc1e56ca113f39b343ee6f0e9dc3f43f0c1c77fd9c1af6ffdd10bdf7e3346dbe","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":"2026-08-08T03:34:15.326354Z","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":"sha256:9c80699095be3c41ec78b94fd287370f984b73906df6af5a9d15d3564034dcb2","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":"2026-08-08T03:34:15.326354Z","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:d9cf32c34b859a7899a2996be94e94b83b7601509f24dc0d47b541df4fc21c75","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":"2026-08-08T03:34:15.326354Z","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:dc19e629be1c7c813df4e313f7bdd4f0cabc61b7608b60e0fd1df04b9c2cfba1","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":"2026-08-07T12:57:31.108845Z","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":"2026-08-08T03:34:15.326354Z","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":"sha256:9327f15eead2e253fdc1f1e1cd77eb928ca4d86334423aa99052b214bda9e806","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":"2026-08-10T03:16:39.751115Z","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":"2026-08-08T03:34:15.326354Z","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:e01eb1cda64c006c4cd498df59be0f27e9af8d3af0dd27a337afbe3dbf91272c","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8e6a38c6c4642e61586970332526720e108222d9078372ee274cc6226aa4199b","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":"2026-08-08T03:34:15.326354Z","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":"sha256:24bd1884bca282a708f681cfde6ea12ee957ab343bec698a4dc1f88a3c5333d1","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":"2026-08-08T03:34:15.326354Z","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:e1491f2f410db41fd9e5893d27ba862221745fe2375a2002630c69d9b4f558f7","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":"2026-08-08T03:34:15.326354Z","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":"sha256:5ee74a5042f63fd6ea660878793559ea2af976a3cf4657eb21b91a14d3aa165b","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":"2026-08-08T03:34:15.326354Z","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":"sha256:9aa527623f870883a4581244481ee7f0b97f68638a9bdbb2c00d1d41f1ba320f","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":"2026-08-08T03:34:15.326354Z","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":"sha256:72e2c0eb4c48b9ef89aa024733a31433f34590f2ca2ed1dfbf41457508810419","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":"2026-08-08T00:17:50.884640Z","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":"2026-08-08T03:34:15.326354Z","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":"sha256:3d4ce4524508b1eccfd849583ff0168b5b32b2181f9822d54f7e67e196f497f3","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":"2026-08-09T12:44:42.705428Z","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8d1e17949fd86addbcf87c312d1983208a31a4de27f173a91c9a19a482c687e5","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":"2026-08-08T03:34:15.326354Z","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:c8a0739c279fff10514307d667768562a131a35e0e34a98c75d6aa2ea912e597","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":"2026-08-08T03:34:15.326354Z","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":"sha256:018bf00512b7696528d4d348e5327de432d985b96d40f201afde741d84c28b2a","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8820c258cc259ff424e5cd62933387e747b38994a12ba812018fbf57a9a635c1","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":"2026-08-08T03:34:15.326354Z","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":"sha256:42b5eebe99ea42d0933b1b397da93c33c1c6b676a97ba30c4ea93e103e551f98","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":"2026-08-08T03:34:15.326354Z","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":"sha256:88778c23bd3ee0135f7e7a8c4d20f807da0496be0a09e4e2363321ca414305a3","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":"2026-08-08T03:34:15.326354Z","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:e496ba1e780bcd6a79ca347b16f6d5e08143c9a0e74d654ff7b1d4aef9f101f8","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":"2026-08-08T03:34:15.326354Z","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:df0d2ad119b35be73b582ea6b4781ed3c5a7264d4ec2e3be3ef83df304277464","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8ce1fd07ec0bec2b214cff243aecf99782bec459409ad63ea81e2845a2330c57","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":"2026-08-08T03:34:15.326354Z","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":"sha256:7e7c6a9e7c3db5423571b9c5fc5f4ed13af17cd7d35ac5eabf790ba53218d29a","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":"2026-08-08T03:34:15.326354Z","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":"sha256:3b1cf1578581d49367c225a2b673b8d3b4cd63bf109585ff40b71983c2daf5bd","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":"2026-08-08T03:34:15.326354Z","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":"sha256:7e17017260ca12baeb1c886cfe07277a587deedc865f61392cf045d246ae8f4d","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":"2026-08-08T03:34:15.326354Z","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":"sha256:7cf3ec9223bdac5e9c40d3392c47a8a63c1a272a7aa74826ecaf7b9c80a451fd","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":"2026-08-08T03:34:15.326354Z","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":"sha256:55b414662e0f5cccb61c7d433b62ca0e4ba536729419b1268704cb7318991df2","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":"2026-08-08T00:07:43.301473Z","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":"2026-08-08T03:34:15.326354Z","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:d65194fb78e57cff9b6bbdda727b9abe8756cf09ec6a7c38f269de074f81a871","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":"2026-08-08T03:34:15.326354Z","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:e2d43d610491139833dea55a1cbd5a05e503588b8afe871ae72d693b45aaa6ae","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":"2026-08-08T03:34:15.326354Z","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":"sha256:859b5e7e20e7370dacd69931c665d6cfb0b0cbbeeae4c9d9772f0f1188f3bbad","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":"2026-08-08T03:34:15.326354Z","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":"sha256:19e060a289a7872db340e793e3626471b78c8238fdd6ee1fa7e7ab683a7de191","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2f209277313c9ce8ce520442fb7844755d8f7676cf8fe17cce474a12b8fcd118","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":"2026-08-08T03:34:15.326354Z","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":"sha256:2809cb3d56a19ca589ea73f964f93d5b1ff803bbb11962026da889ee456c05d0","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":"2026-08-08T03:34:15.326354Z","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:b9c41ad73654c7bd297dad2eadd61344dc82877e2c52685561c6ecefca28051a","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":"2026-08-08T03:34:15.326354Z","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":"sha256:54932a1655f4cec52007e5685728db6ba0b4bb74016e155d9e693e9a3f28302a","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":"2026-08-08T03:34:15.326354Z","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":"sha256:60731463e17c0c7ab134099cdc550bd5792c2a7628cafa08fc5d39b485c298b9","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":"2026-08-08T03:34:15.326354Z","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":"sha256:0f137b3ab82439c822c221543736d90c41cd1f9dc4926c9803af329064891144","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":"2026-08-08T03:34:15.326354Z","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:d57017616835da68a6c03306d03af9e309c8ffc9fde339f87b4ceac51720319e","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8712049984a85a44f4b22e33d81e1b76606345e410c8e4abcc40ec1caafedf01","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":"2026-08-08T03:34:15.326354Z","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":"sha256:6dc34234b0d11285a1a9f19cd1d992d1f4d6f23a0006943c4f826ed59d4c1c26","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":"2026-08-08T03:34:15.326354Z","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":"sha256:8f95dd9bcf236256f296dc21b58874c65a9ee7546e5124455afa62c2bd53ead5","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":"2026-08-08T03:34:15.326354Z","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":"sha256:852011dd3794c46dc0001fd11072bc6d5ab5501149535b0458c6dbd9f50babfc","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":"2026-08-08T03:34:15.326354Z","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":"sha256:9f0e7321fb2ebfd675c3b42640aef0057a784624e05fc3c461fb0a7e0e4a17ec","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":"2026-08-08T03:34:15.326354Z","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":"sha256:4109f0cb5b9d85044c3219a33c08d9ff2b1b9ab57ee7174dd0a5702b50923da4","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":"2026-08-08T03:34:15.326354Z","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:bf411387ce964b2b2dea9711a16c9cacf33b8cb714bcc4576272b08a5fcf9dbe","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":"2026-08-08T03:34:15.326354Z","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":"sha256:0bb8f67b52d8e97febf2b80354e81d11f8f163d10def4f5b517d09303f96271f","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":"2026-08-08T03:34:15.326354Z","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:affc7d85e59f9474fa0380816f55eee2204d7626815dff19589e3f33a0972ebc","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":"2026-08-08T03:34:15.326354Z","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":"sha256:23d9a133ac9fcaea1dfe5f5980473bfab086e64898b98e564a1403c628d1067b","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":"2026-08-08T03:34:15.326354Z","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":"sha256:10f2eb1bcb289a8613be1cddc29f8aa30d6693061f96878c58b614aa321883bf","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":"2026-08-08T03:34:15.326354Z","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":"sha256:4161325f0624e42826ee00033d25a2a1ed4e0ac5a995a8ec9e1094359112ce30","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":"2026-08-08T03:34:15.326354Z","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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 0 inbound Pith citation observations for arXiv:2604.04290."}