{"as_of":"2026-08-09T19:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0c813415ca048b97a672013458b7afe292bf9f0bc81c72447afbb0a87fc4675e","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T13:19:35.189618Z","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":27,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":"2106.04399","doi":"10.48550/arxiv.2106.04399","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"22ddef23-6e3c-4d35-9de6-eafd26e6db92","year":2021},"citing_paper":{"arxiv_id":"2309.16797","last_updated":"2023-09-28T19:01:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-28T19:01:07Z","title":"Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-16T08:12:30.984870Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2309.16797"},"observation_digest":"sha256:c2702ce6a2dbddb1050126ad06d06efe948135a85bc730a6e46221447293c828","observation_id":"cc1e7747-00b1-41c7-afd8-7013e307e433","resolution":{"observed_at":"2026-05-16T08:12:31.295643Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-09T13:19:35.189618Z","title":"Bengio, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02127","last_updated":"2025-02-04T09:02:41Z","snapshot_observed_at":"2026-08-09T13:11:25.074378Z","submitted_at":"2025-02-04T09:02:41Z","title":"Exploring Generative Networks for Manifolds with Non-Trivial Topology","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T13:19:35.189618Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2502.02127"},"observation_digest":"sha256:ea2e21639033a27024708dc0d5cba417fa4d5acbe1d56da5a2a8d99847b51055","observation_id":"3c38f39b-5531-41aa-aa07-2024313cb446","resolution":{"observed_at":"2026-08-09T13:19:35.189618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-07T14:20:02.633092Z","title":"Gflownet","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.19431","last_updated":"2025-05-26T02:48:26Z","snapshot_observed_at":"2026-08-07T14:11:55.796750Z","submitted_at":"2025-05-26T02:48:26Z","title":"Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:02.633092Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2505.19431"},"observation_digest":"sha256:f31e18ab6593eb97a0597d33b46602e60bc5b1937b4c03847ba7c3600bfdb5d0","observation_id":"a5e75809-8fb8-4ca7-a574-ad4896ea483f","resolution":{"observed_at":"2026-08-07T14:20:02.633092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-06T21:39:19.073432Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Genera- tion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24021","last_updated":"2025-06-30T16:24:21Z","snapshot_observed_at":"2026-08-06T21:23:43.719898Z","submitted_at":"2025-06-30T16:24:21Z","title":"Minimally dissipative multi-bit logical operations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:39:19.073432Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2506.24021"},"observation_digest":"sha256:6b94165ee6afa584aa5aa999d509226b3181fd584cb07f79ac0b8884674a4002","observation_id":"b6171fbb-addc-4fe8-835b-f17f62f9b961","resolution":{"observed_at":"2026-08-06T21:39:19.073432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":"2106.04399","doi":"10.48550/arxiv.2106.04399","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"22ddef23-6e3c-4d35-9de6-eafd26e6db92","year":2021},"citing_paper":{"arxiv_id":"2605.02300","last_updated":"2026-05-04T07:48:02Z","snapshot_observed_at":"2026-07-06T23:15:23.301944Z","submitted_at":"2026-05-04T07:48:02Z","title":"A Meta Reinforcement Learning Approach to Goals-Based Wealth Management","version":1},"reference_index":258,"source":"arxiv_source","source_observed_at":"2026-05-08T18:42:50.962120Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2605.02300"},"observation_digest":"sha256:4e582dc0db403bb51acdb0045b52273276b24c5fd0259f6966a6c4197a1c9230","observation_id":"1a496d5e-5412-4c1a-9424-cd97e020d32f","resolution":{"observed_at":"2026-05-08T18:44:01.652301Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":"2106.04399","doi":"10.48550/arxiv.2106.04399","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"22ddef23-6e3c-4d35-9de6-eafd26e6db92","year":2021},"citing_paper":{"arxiv_id":"2605.26540","last_updated":"2026-08-06T10:54:40Z","snapshot_observed_at":"2026-08-09T19:10:07.416392Z","submitted_at":"2026-05-26T04:43:45Z","title":"Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-01T16:52:26.323828Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2605.26540"},"observation_digest":"sha256:4c248737072bae7a66cec856740e97d50bd7a1bf9e9ac3a4c59e493176be8d85","observation_id":"39d05a94-a868-461e-9ab8-7beb1f87b4e6","resolution":{"observed_at":"2026-07-01T16:55:50.488782Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.04399","snapshot_observed_at":"2026-08-08T15:33:58.198605Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05314","last_updated":"2026-08-05T18:15:19Z","snapshot_observed_at":"2026-08-09T19:10:59.812830Z","submitted_at":"2026-08-05T18:15:19Z","title":"Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-08T15:33:58.198605Z"},"links":{"cited_paper":"/paper/2106.04399","citing_paper":"/paper/2608.05314"},"observation_digest":"sha256:8acbda505c95e089d4d404d9850061cc4e4fca842d704713a9d2ea6a190b3f64","observation_id":"95334be4-9206-4d81-80e0-168a0c438259","resolution":{"observed_at":"2026-08-08T15:33:58.198605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.04399/citation-record","integrity":"/paper/2106.04399/integrity","json":"/paper/2106.04399/citation-record.json","paper":"/paper/2106.04399"},"outbound":[],"paper":{"arxiv_id":"2106.04399","last_updated":"2021-11-19T14:50:32Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:17:08.675456Z","submitted_at":"2021-06-08T14:21:10Z","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2106.04399."}