{"as_of":"2026-08-08T18:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e5436667ee8a3de955d49463360e82ff7b572ee202039698032e49bd2645157f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:44:27.549180Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":18,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-07T05:44:27.549180Z","title":"Conditional sig-wasserstein gans for time series generation.arXiv preprint arXiv:2006.05421, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.07299","last_updated":"2025-06-08T21:55:00Z","snapshot_observed_at":"2026-08-07T05:34:33.953044Z","submitted_at":"2025-06-08T21:55:00Z","title":"Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T05:44:27.549180Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2506.07299"},"observation_digest":"sha256:c817f200df2abcbaf17bd6c56d6e7c3dfead3ec028c3383eee0c777895454c21","observation_id":"ca8eaa79-83ed-457a-9039-7e90c4529ad3","resolution":{"observed_at":"2026-08-07T05:44:27.549180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-06T05:26:03.617765Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.02758","last_updated":"2025-08-03T17:07:08Z","snapshot_observed_at":"2026-08-07T12:37:52.841319Z","submitted_at":"2025-08-03T17:07:08Z","title":"CTBench: Cryptocurrency Time Series Generation Benchmark","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T05:26:03.617765Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2508.02758"},"observation_digest":"sha256:3876e2258f17a731f016b84551162c3c49a5aed9bea297722291c498fd9ddd83","observation_id":"320c41fc-e473-4716-b6d2-b9692cf853fa","resolution":{"observed_at":"2026-08-06T05:26:03.617765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2602.17071","last_updated":"2026-04-11T12:06:12Z","snapshot_observed_at":"2026-08-01T04:28:59.324747Z","submitted_at":"2026-02-19T04:26:57Z","title":"AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-15T21:30:43.925179Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2602.17071"},"observation_digest":"sha256:291f6ce3699c5e3dc45135478d5577729653c300b77b55719012daa4df3205b6","observation_id":"2a6135e0-3482-4c50-bc31-6b144db36441","resolution":{"observed_at":"2026-05-15T21:31:39.353543Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2604.04662","last_updated":"2026-04-06T13:15:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-06T13:15:44Z","title":"Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T19:28:16.659393Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2604.04662"},"observation_digest":"sha256:14670b964a7f086f85b8e64068ab2ffe00fee95acce9796e0fac3531ba891ff2","observation_id":"f61d0d89-7097-4ed7-9c80-233c5cbf9fc7","resolution":{"observed_at":"2026-05-10T22:55:49.975226Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2604.05008","last_updated":"2026-04-06T13:23:59Z","snapshot_observed_at":"2026-07-06T22:53:51.418227Z","submitted_at":"2026-04-06T13:23:59Z","title":"Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T19:27:38.760443Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2604.05008"},"observation_digest":"sha256:da17ef12b3ee8d511cd41e49d41b0f1c99dc947524a041295e51d62dfeddbddb","observation_id":"6047b9f6-44a6-4b02-86b1-9eab5a8e09ff","resolution":{"observed_at":"2026-05-10T22:55:51.748347Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2604.27182","last_updated":"2026-04-29T20:32:45Z","snapshot_observed_at":"2026-07-06T23:12:43.284564Z","submitted_at":"2026-04-29T20:32:45Z","title":"Preserving Temporal Dynamics in Time Series Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-07T08:19:34.915689Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2604.27182"},"observation_digest":"sha256:d56703f1cbc56cbf122a922a563a6d5225abefd1b4fb986100230518f557f25d","observation_id":"9e0c692d-6fb6-418c-8974-f06110652ff2","resolution":{"observed_at":"2026-05-12T10:01:28.923816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2605.28355","last_updated":"2026-05-27T11:54:35Z","snapshot_observed_at":"2026-08-06T09:11:02.459445Z","submitted_at":"2026-05-27T11:54:35Z","title":"Detecting Diffusion-Generated Time Series Under Generator Shift","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T13:45:25.374526Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2605.28355"},"observation_digest":"sha256:ba83cc34b98ddf64b4dd92b4de840042203bec95f3891ceb3d5c8f41102d95b2","observation_id":"9c9e7d87-338a-4975-ba20-6d01fff2f884","resolution":{"observed_at":"2026-06-29T13:53:28.928648Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2605.28867","last_updated":"2026-05-22T07:10:20Z","snapshot_observed_at":"2026-08-04T16:22:45.174745Z","submitted_at":"2026-05-22T07:10:20Z","title":"PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-30T16:27:45.767100Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2605.28867"},"observation_digest":"sha256:59321aa0cbd6af12c3e73b28bdd428791e0a3b767d51ad03fcdb1a779cd58ca8","observation_id":"59ccbb4a-ec01-4aa5-b70e-8a77d7a6278a","resolution":{"observed_at":"2026-06-30T16:35:12.730898Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation","version":2},"cited_work":{"arxiv_id":"2006.05421","doi":"10.48550/arxiv.2006.05421","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.05421","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Conditional sig-wasserstein gans for time series generation","venue":"arXiv (Cornell University)","work_id":"bc98d5c2-f02d-476c-a6d9-f63c23f50bbc","year":2006},"citing_paper":{"arxiv_id":"2606.23492","last_updated":"2026-06-22T15:39:33Z","snapshot_observed_at":"2026-08-06T19:47:36.081938Z","submitted_at":"2026-06-22T15:39:33Z","title":"Continuous Hidden Markov Models for Equity Returns: Heavy-Tail Emission Families and Regime-Conditional Value-at-Risk","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-26T01:56:25.909340Z"},"links":{"cited_paper":"/paper/2006.05421","citing_paper":"/paper/2606.23492"},"observation_digest":"sha256:5bd603d9464eb5e651fa4f67e12571cfd8681b7cdd6facb11878d22047e701d9","observation_id":"bdcf0593-79c1-4af4-97f1-e2a969825c3d","resolution":{"observed_at":"2026-06-26T01:58:54.092796Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.05421/citation-record","integrity":"/paper/2006.05421/integrity","json":"/paper/2006.05421/citation-record.json","paper":"/paper/2006.05421"},"outbound":[],"paper":{"arxiv_id":"2006.05421","last_updated":"2023-10-11T20:39:34Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:27:25.685302Z","submitted_at":"2020-06-09T17:38:55Z","title":"Conditional Sig-Wasserstein GANs for Time Series Generation"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2006.05421."}