{"as_of":"2026-08-21T11:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f03f7696991c083e52eb24ff025b174288e70c05179b8f4a592263a59fc8387","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":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":58,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:46:47.166031Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":71,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2407.20067","last_updated":"2024-11-08T17:49:46Z","snapshot_observed_at":"2026-08-12T12:09:26.275357Z","submitted_at":"2024-07-29T14:53:45Z","title":"xAI-Drop: Don't Use What You Cannot Explain","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-23T22:59:50.222196Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2407.20067"},"observation_digest":"sha256:78e6f1bd048c740e8cfebb5fed0e39f645f15efdc784cdce4dd82f8781da2f53","observation_id":"e5a23cc7-a32f-45fa-8f5a-d3e76471cf96","resolution":{"observed_at":"2026-05-23T23:03:34.628980Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-12T16:37:40.938756Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.13358","last_updated":"2024-11-20T14:29:59Z","snapshot_observed_at":"2026-08-16T14:14:20.471795Z","submitted_at":"2024-11-20T14:29:59Z","title":"Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T16:37:40.938756Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2411.13358"},"observation_digest":"sha256:02d726c74c226ab1f6b8c2f24aedf190f3765cbaf7bb20f3363d2cdca6a5cb35","observation_id":"16241126-05f6-4119-8549-a5c13116f66e","resolution":{"observed_at":"2026-08-12T16:37:40.938756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-12T12:28:06.807516Z","title":"arXiv preprint arXiv:2209.14734 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17196","last_updated":"2025-03-04T01:38:11Z","snapshot_observed_at":"2026-08-18T18:18:45.411339Z","submitted_at":"2024-11-26T08:10:12Z","title":"P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:28:06.807516Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2411.17196"},"observation_digest":"sha256:d7d49715ec92f8a5570adeabedc0c7ecd735bb65c9bf5f7366f76b54f19f7064","observation_id":"600b374d-b951-49c6-a207-a91f30382ab4","resolution":{"observed_at":"2026-08-12T12:28:06.807516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T18:13:12.680137Z","title":"InThe 28th International Conference on Artificial Intelligence and Statistics","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08174","last_updated":"2025-06-01T19:26:04Z","snapshot_observed_at":"2026-08-18T00:53:43.264476Z","submitted_at":"2024-12-11T08:03:35Z","title":"Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T18:13:12.680137Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.08174"},"observation_digest":"sha256:7b420276760109fa699087fb020b3020ba39da60fc9352f94168da7b35f46af8","observation_id":"6374d574-436e-477a-944e-299c1efc2aa0","resolution":{"observed_at":"2026-08-11T18:13:12.680137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T16:23:51.793586Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.10193","last_updated":"2025-05-27T20:06:29Z","snapshot_observed_at":"2026-08-18T11:31:34.951890Z","submitted_at":"2024-12-13T15:08:30Z","title":"Simple Guidance Mechanisms for Discrete Diffusion Models","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T16:23:51.793586Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.10193"},"observation_digest":"sha256:74d1a05b378bed9259a99d3005436042c148362ee3036b42586aa4c955e0700d","observation_id":"7aeda284-eede-42bb-8101-60ec94da1007","resolution":{"observed_at":"2026-08-11T16:23:51.793586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T19:57:43.544318Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.10415","last_updated":"2024-12-09T06:58:17Z","snapshot_observed_at":"2026-08-15T09:28:46.290713Z","submitted_at":"2024-12-09T06:58:17Z","title":"Generative Adversarial Reviews: When LLMs Become the Critic","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T19:57:43.544318Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.10415"},"observation_digest":"sha256:46c7f2d2116f8ebf51eedbd86624455f0407e7eb86dbea909330cdaf18ca0738","observation_id":"d59f5011-28bd-4ad3-84ba-285be432a115","resolution":{"observed_at":"2026-08-11T19:57:43.544318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T10:36:19.462020Z","title":"Digress: Discrete denoising diffusion for graph generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16512","last_updated":"2024-12-21T07:21:53Z","snapshot_observed_at":"2026-08-20T01:59:38.957965Z","submitted_at":"2024-12-21T07:21:53Z","title":"TrojFlow: Flow Models are Natural Targets for Trojan Attacks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:36:19.462020Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.16512"},"observation_digest":"sha256:f9ba7cf42a40bcb932f9854ad95a1a947888ea92f909849e2dd78e5bd261d44b","observation_id":"7e2c9739-a6b2-4130-b549-c1fd9b35d7a4","resolution":{"observed_at":"2026-08-11T10:36:19.462020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T10:24:32.423973Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16699","last_updated":"2024-12-21T16:57:09Z","snapshot_observed_at":"2026-08-20T11:40:58.408869Z","submitted_at":"2024-12-21T16:57:09Z","title":"FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion Generation","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T10:24:32.423973Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.16699"},"observation_digest":"sha256:183efc7367edb1da666fc6c49f7498c01d44ee0c9714f32c1a5812669df6871e","observation_id":"de6f8763-af29-49c9-b7f5-40d0ebd71a5f","resolution":{"observed_at":"2026-08-11T10:24:32.423973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-11T18:44:52.142348Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.19812","last_updated":"2025-01-27T05:43:48Z","snapshot_observed_at":"2026-08-18T21:41:51.410285Z","submitted_at":"2024-12-10T15:41:06Z","title":"Pharmacophore-guided de novo drug design with diffusion bridge","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T18:44:52.142348Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2412.19812"},"observation_digest":"sha256:4b41f8595c85849f1ac198e22d30a8ff1199a5eded09d03adb91adb986974beb","observation_id":"d91cfa0d-f930-4bac-a245-4d90a5a74374","resolution":{"observed_at":"2026-08-11T18:44:52.142348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-10T22:40:48.820863Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01073","last_updated":"2025-06-03T16:53:33Z","snapshot_observed_at":"2026-08-15T15:11:09.002273Z","submitted_at":"2025-01-02T05:44:11Z","title":"Graph Generative Pre-trained Transformer","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-10T22:40:48.820863Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2501.01073"},"observation_digest":"sha256:644a5d6ca3921cc9e4b4e2088c7fb43f6c03187b65e0f0fb564b73b31dfe07b8","observation_id":"3b19083e-3c99-4a35-bf3a-98fc9101d4d1","resolution":{"observed_at":"2026-08-10T22:40:48.820863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-10T20:58:07.145776Z","title":"Digress: Discrete denoising diffusion for graph generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.06756","last_updated":"2026-06-16T07:11:33Z","snapshot_observed_at":"2026-08-14T21:08:33.129981Z","submitted_at":"2025-01-12T09:02:32Z","title":"Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T20:58:07.145776Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2501.06756"},"observation_digest":"sha256:c6f7c37ee3a56f17b0948e3812d0ba68eff4d658ba6f7f43a9057038a703f749","observation_id":"dd454f91-6520-4830-9e3f-740858f94c0c","resolution":{"observed_at":"2026-08-10T20:58:07.145776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2501.11568","last_updated":"2026-04-10T10:01:42Z","snapshot_observed_at":"2026-08-14T06:21:24.606630Z","submitted_at":"2025-01-20T16:18:40Z","title":"Graph Defense Diffusion Model","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-23T04:50:28.600618Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2501.11568"},"observation_digest":"sha256:4312d0816aab0537139768664d93df39cef7b1fa0553c5298ae29f0e551c047d","observation_id":"922e2823-2ce4-4858-9148-2fabe1e13dae","resolution":{"observed_at":"2026-05-23T04:52:34.239698Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-09T15:29:27.675179Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01416","last_updated":"2025-08-16T08:29:31Z","snapshot_observed_at":"2026-08-16T18:04:18.861986Z","submitted_at":"2025-02-03T14:55:28Z","title":"Categorical Schr\\\"odinger Bridge Matching","version":4},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-09T15:29:27.675179Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2502.01416"},"observation_digest":"sha256:c921022d936feb5e4b3295f3254d831c8448ade03556b18ec9363d272748d491","observation_id":"128bc757-3941-4938-bfe2-ea53fc0b73de","resolution":{"observed_at":"2026-08-09T15:29:27.675179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-09T12:09:59.433936Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02488","last_updated":"2025-02-04T17:04:16Z","snapshot_observed_at":"2026-08-18T10:59:20.705996Z","submitted_at":"2025-02-04T17:04:16Z","title":"Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T12:09:59.433936Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2502.02488"},"observation_digest":"sha256:c8f86d6a45d35046a0576c922af7a37e3ae06cd5ffe558f244f3f508f5be761f","observation_id":"c22c8a3d-2576-453a-b314-88cb9ac72820","resolution":{"observed_at":"2026-08-09T12:09:59.433936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T20:56:23.257925Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.09622","last_updated":"2025-06-09T02:36:10Z","snapshot_observed_at":"2026-08-19T08:25:52.697066Z","submitted_at":"2025-02-13T18:59:47Z","title":"Theoretical Benefit and Limitation of Diffusion Language Model","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T20:56:23.257925Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2502.09622"},"observation_digest":"sha256:38cb2cc27c542e17bc530fce4aba35b0a217fd252cb188d408651ec8e9eda412","observation_id":"06dd4555-62ce-4838-9254-c6753cdadc98","resolution":{"observed_at":"2026-08-07T20:56:23.257925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T15:20:09.326789Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15870","last_updated":"2025-05-21T13:17:34Z","snapshot_observed_at":"2026-08-19T23:33:52.308828Z","submitted_at":"2025-05-21T13:17:34Z","title":"Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T15:20:09.326789Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2505.15870"},"observation_digest":"sha256:ae274a904d3cf94778180eceac3591e40e33fe7ab2cf5bc6eac78790f7ad405e","observation_id":"7bffd98d-a779-4b86-ba97-c030e23986a3","resolution":{"observed_at":"2026-08-07T15:20:09.326789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T15:20:12.781839Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.17111","last_updated":"2025-05-21T12:56:40Z","snapshot_observed_at":"2026-08-18T02:08:39.956205Z","submitted_at":"2025-05-21T12:56:40Z","title":"A Global Commuting Origin-Destination Flow Dataset for Urban Sustainable Development","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T15:20:12.781839Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2505.17111"},"observation_digest":"sha256:7088d2360faf462b55367ac2819ec98d53580b6d8d2177e16fe52b3f00d3eb7a","observation_id":"d515951a-6e2c-4a4d-a3b4-a67f3cfd0526","resolution":{"observed_at":"2026-08-07T15:20:12.781839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T11:19:35.843845Z","title":"Di- gress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02781","last_updated":"2025-06-03T12:01:41Z","snapshot_observed_at":"2026-08-15T22:18:32.665184Z","submitted_at":"2025-06-03T12:01:41Z","title":"FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:19:35.843845Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2506.02781"},"observation_digest":"sha256:4219b97fff344712490df36bb86dca92829733a2b448e02bf80e6bf673ab7261","observation_id":"6c310bf8-2c59-4649-8b8b-5592bb5c3704","resolution":{"observed_at":"2026-08-07T11:19:35.843845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T05:50:58.658384Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06915","last_updated":"2025-06-07T20:29:59Z","snapshot_observed_at":"2026-08-15T02:55:29.707992Z","submitted_at":"2025-06-07T20:29:59Z","title":"Graph Neural Networks in Modern AI-aided Drug Discovery","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:50:58.658384Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2506.06915"},"observation_digest":"sha256:b9363a81322d24b22a9a9f75139ccdc49d469b2119b38b99640f7d61fdc9d246","observation_id":"bcfa75b6-6537-4937-af54-fbe7eb05c77b","resolution":{"observed_at":"2026-08-07T05:50:58.658384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-07T04:44:57.427131Z","title":", Krawczuk, I","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09832","last_updated":"2025-06-11T15:10:41Z","snapshot_observed_at":"2026-08-19T17:28:00.232776Z","submitted_at":"2025-06-11T15:10:41Z","title":"A Deep Generative Model for the Simulation of Discrete Karst Networks","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T04:44:57.427131Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2506.09832"},"observation_digest":"sha256:432d69875437eff39da814df8c22c0f39b6ef178f3e5d633d4993993bda5741a","observation_id":"06992cab-fa3d-48ef-a4e0-7371f329df1d","resolution":{"observed_at":"2026-08-07T04:44:57.427131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T21:21:13.733863Z","title":"Digress: Discrete denoising diffusion for graph generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00444","last_updated":"2025-07-19T03:51:06Z","snapshot_observed_at":"2026-08-10T19:54:34.089329Z","submitted_at":"2025-07-01T05:54:31Z","title":"DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:21:13.733863Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.00444"},"observation_digest":"sha256:17a30506569c242cbcd399003dd760815de4a2f1334fa4bdefd75d8b34d321d2","observation_id":"8eecaf5b-dfc8-45ff-8788-c2b5bdc10b32","resolution":{"observed_at":"2026-08-06T21:21:13.733863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T20:44:42.936745Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02151","last_updated":"2025-07-02T21:15:00Z","snapshot_observed_at":"2026-08-11T08:59:35.120284Z","submitted_at":"2025-07-02T21:15:00Z","title":"Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:44:42.936745Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.02151"},"observation_digest":"sha256:50a970f436fb99883679c92fa709cec39ac39c641c6350c5c6af9db4a85c87d4","observation_id":"c089b52a-8fca-41a9-b3af-5fd7c59f61d1","resolution":{"observed_at":"2026-08-06T20:44:42.936745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T20:01:04.254852Z","title":"Digress: Discrete Denoising Diffusion for Graph Genera- tion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04081","last_updated":"2026-06-18T10:02:51Z","snapshot_observed_at":"2026-08-08T22:57:04.051206Z","submitted_at":"2025-07-05T16:07:05Z","title":"Graph Diffusion-Based AeBS Deployment and Resource Allocation in RSMA-Enabled URLLC Low-Altitude Wireless Networks","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:01:04.254852Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.04081"},"observation_digest":"sha256:4e66a643bb05593012c39cf328e9fa7da003e53b657b84b1760c3a9feeccb5c7","observation_id":"178beddd-79af-45f4-b26c-e75e0d35f705","resolution":{"observed_at":"2026-08-06T20:01:04.254852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T19:44:20.075829Z","title":"Vignac, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.04765","last_updated":"2025-07-07T08:43:46Z","snapshot_observed_at":"2026-08-16T23:44:48.848582Z","submitted_at":"2025-07-07T08:43:46Z","title":"GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:44:20.075829Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.04765"},"observation_digest":"sha256:1408f79556cd516171015b960948f6e2463cc9e7390ca5cd8452fc34b8e34abb","observation_id":"1df43017-957f-4751-962c-e2dc42d6d203","resolution":{"observed_at":"2026-08-06T19:44:20.075829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T19:42:34.092336Z","title":"arXiv preprint arXiv:2209.14734 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04856","last_updated":"2025-07-07T10:29:54Z","snapshot_observed_at":"2026-08-08T23:34:37.655753Z","submitted_at":"2025-07-07T10:29:54Z","title":"Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:42:34.092336Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.04856"},"observation_digest":"sha256:d7778c45ba61504b741f68e2d235d0ddf275c4fd2a32b7425b91d955f1732d2c","observation_id":"d7502526-bb13-4906-83dc-8838c811272f","resolution":{"observed_at":"2026-08-06T19:42:34.092336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T19:04:16.038202Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08854","last_updated":"2025-07-09T06:21:36Z","snapshot_observed_at":"2026-08-18T21:52:08.655338Z","submitted_at":"2025-07-09T06:21:36Z","title":"DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:04:16.038202Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.08854"},"observation_digest":"sha256:34b275c487fc8499a92760d5a8ae6ab854ec92a8d8e76d111678f1bdfd3237a8","observation_id":"a1cd5714-a4a6-4c46-a6d8-cfc091161eca","resolution":{"observed_at":"2026-08-06T19:04:16.038202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T17:10:48.918797Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.11710","last_updated":"2025-07-15T20:30:16Z","snapshot_observed_at":"2026-08-13T22:23:45.536624Z","submitted_at":"2025-07-15T20:30:16Z","title":"Subgraph Generation for Generalizing on Out-of-Distribution Links","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:48.918797Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.11710"},"observation_digest":"sha256:caed9737aeea0a38bb134c6f96cb52cb772a2751ca0ab54ab28502288f6e7d25","observation_id":"65d62021-ff28-4eb6-8a58-e0321ca392fe","resolution":{"observed_at":"2026-08-06T17:10:48.918797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-06T16:36:22.159952Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13133","last_updated":"2025-07-17T13:54:42Z","snapshot_observed_at":"2026-08-17T04:17:28.450747Z","submitted_at":"2025-07-17T13:54:42Z","title":"NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:36:22.159952Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.13133"},"observation_digest":"sha256:95b3294d78e08251f01c85a324575b49c8a13d512062d7e61eb6d69ba889b5cb","observation_id":"0181e668-ede3-4d1a-bf0c-11c2025640ed","resolution":{"observed_at":"2026-08-06T16:36:22.159952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-15T17:46:47.166031Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.20578","last_updated":"2025-07-28T07:22:06Z","snapshot_observed_at":"2026-08-18T21:42:48.341983Z","submitted_at":"2025-07-28T07:22:06Z","title":"Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T17:46:47.166031Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2507.20578"},"observation_digest":"sha256:99f7679f144fa67875bb9b35d3273145ee0258173d8bc0aa6acaac46f5ec3061","observation_id":"92224ec5-d63f-4fbe-9a9e-e1742b30096f","resolution":{"observed_at":"2026-08-15T17:46:47.166031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2508.08441","last_updated":"2026-05-09T03:36:43Z","snapshot_observed_at":"2026-08-18T21:01:34.962007Z","submitted_at":"2025-08-04T13:33:38Z","title":"SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data","version":3},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-19T01:11:16.861758Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2508.08441"},"observation_digest":"sha256:b35007d181d468a5c5e0a491c1b28cac9994a39c92a371a85b210d292cda0764","observation_id":"8f9301b7-b925-44f2-9b25-c2e172c6a133","resolution":{"observed_at":"2026-05-19T01:11:57.019734Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-15T17:25:08.337074Z","title":"Vignac, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.12629","last_updated":"2025-08-18T05:13:27Z","snapshot_observed_at":"2026-08-18T18:10:05.818208Z","submitted_at":"2025-08-18T05:13:27Z","title":"FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T17:25:08.337074Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2508.12629"},"observation_digest":"sha256:bb4484de4ef7e6c82689cdfecd8cd634f4cedadd93b92f3de02943c7eac53929","observation_id":"1a088320-1061-4010-a3ee-1814d023f0c3","resolution":{"observed_at":"2026-08-15T17:25:08.337074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T18:43:46.757680Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.14352","last_updated":"2025-08-20T01:47:46Z","snapshot_observed_at":"2026-08-15T19:30:02.922687Z","submitted_at":"2025-08-20T01:47:46Z","title":"SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-05T18:43:46.757680Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2508.14352"},"observation_digest":"sha256:264a5c9985499ec1ce1c7bd09308efbc78d815fdabfc3e3fc8106bf221981a58","observation_id":"e29653dd-e1a5-4298-85a5-e9a03f58b3cb","resolution":{"observed_at":"2026-08-05T18:43:46.757680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-15T17:05:01.452738Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.17815","last_updated":"2025-08-25T09:12:01Z","snapshot_observed_at":"2026-08-18T18:10:38.053994Z","submitted_at":"2025-08-25T09:12:01Z","title":"Multi-domain Distribution Learning for De Novo Drug Design","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T17:05:01.452738Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2508.17815"},"observation_digest":"sha256:18ede3253060ca04e8d429fce7de774055880eb81b3cb65a37d8006cc8bfbc3f","observation_id":"4bd396fa-51c7-406c-9d8c-ab18e088a3bc","resolution":{"observed_at":"2026-08-15T17:05:01.452738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T16:00:39.595996Z","title":"Digress: Discrete denoising diffusion for graph generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.00071","last_updated":"2025-08-26T15:11:10Z","snapshot_observed_at":"2026-08-05T16:00:38.949929Z","submitted_at":"2025-08-26T15:11:10Z","title":"SynCircuit: Automated Generation of New Synthetic RTL Circuits Can Enable Big Data in Circuits","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T16:00:39.595996Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2509.00071"},"observation_digest":"sha256:7b20d34d1722213b78cf25e3dc3f2bd5ba0f4b057c4b69ea407c30e2999b7af5","observation_id":"5ad16549-b85d-439b-809e-201285405b4f","resolution":{"observed_at":"2026-08-05T16:00:39.595996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2509.17291","last_updated":"2025-09-22T00:10:29Z","snapshot_observed_at":"2026-08-14T12:18:12.638209Z","submitted_at":"2025-09-22T00:10:29Z","title":"GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T13:59:33.946347Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2509.17291"},"observation_digest":"sha256:ec175d68dadb91fc8a9ea38f7c2828f0f6a7505e221cc37d0923eeafe9936a37","observation_id":"f46e3f5b-44b2-42c2-8af7-5a8f9ca11233","resolution":{"observed_at":"2026-05-18T14:01:27.157156Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2510.12916","last_updated":"2026-04-17T19:33:42Z","snapshot_observed_at":"2026-08-16T06:53:13.752166Z","submitted_at":"2025-10-14T18:42:12Z","title":"Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-18T07:14:42.051614Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2510.12916"},"observation_digest":"sha256:95f24515352c7626acc4094c9faa0cbe135bad7cf51ce4bfd12a971963755125","observation_id":"4c29bd8b-6977-4575-afbc-d0acc9d64bb5","resolution":{"observed_at":"2026-05-18T07:16:01.949327Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2511.03015","last_updated":"2026-04-13T04:30:54Z","snapshot_observed_at":"2026-08-16T01:59:19.416700Z","submitted_at":"2025-11-04T21:25:51Z","title":"Discrete Bayesian Sample Inference for Graph Generation","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-18T00:44:01.292176Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2511.03015"},"observation_digest":"sha256:75b0c2ce0ad87e3ad01f7382f884bfd343eea9687613b998703828e94bf0406d","observation_id":"2b75d532-0ae9-405c-8c3c-145aad91e33f","resolution":{"observed_at":"2026-05-18T00:45:33.066603Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2604.11748","last_updated":"2026-04-15T16:15:30Z","snapshot_observed_at":"2026-08-14T05:04:31.628759Z","submitted_at":"2026-04-13T17:21:41Z","title":"LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T16:26:53.241071Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2604.11748"},"observation_digest":"sha256:aaee27c2d1cea4b785ffa28996ee5945c27779ea9a40125cf60897f9fc7e482d","observation_id":"8aaa78ae-6dfe-4e40-81b7-f337b8c55f37","resolution":{"observed_at":"2026-05-11T08:50:59.573908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2604.14705","last_updated":"2026-04-16T07:12:16Z","snapshot_observed_at":"2026-08-17T21:15:33.367235Z","submitted_at":"2026-04-16T07:12:16Z","title":"SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-10T11:34:03.376411Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2604.14705"},"observation_digest":"sha256:048177ff3a85bf294dc914d9964dffe2276293af868de345d35a418031ff95b5","observation_id":"0fa435c8-613b-4088-93ba-c1c045340f9e","resolution":{"observed_at":"2026-05-10T11:35:18.790953Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2604.17310","last_updated":"2026-04-19T07:55:57Z","snapshot_observed_at":"2026-08-13T14:40:07.653597Z","submitted_at":"2026-04-19T07:55:57Z","title":"Interpolating Discrete Diffusion Models with Controllable Resampling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T06:42:27.567344Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2604.17310"},"observation_digest":"sha256:4553e7986be9fdc1486f8524bef767376695141e36cf39206a7750a6a769f4d2","observation_id":"aff2e363-9a10-4ad5-b722-2f2a38814ee0","resolution":{"observed_at":"2026-05-10T06:46:37.467248Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2604.18344","last_updated":"2026-04-20T14:41:47Z","snapshot_observed_at":"2026-08-11T17:41:35.230537Z","submitted_at":"2026-04-20T14:41:47Z","title":"One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T05:11:15.653424Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2604.18344"},"observation_digest":"sha256:c46c3b361b8eb48b7aa4bff677a7a0f53f0b4516042f9fb4abe9d8f9c592ab6e","observation_id":"9c54a24b-e3e7-4bd5-9b25-3ad9b9e36205","resolution":{"observed_at":"2026-05-10T09:38:42.980462Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2605.05689","last_updated":"2026-05-07T05:29:57Z","snapshot_observed_at":"2026-07-06T23:18:17.333660Z","submitted_at":"2026-05-07T05:29:57Z","title":"GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T11:46:42.010486Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2605.05689"},"observation_digest":"sha256:082419f7420d9fb947b99fb8d8f2e72cb22bc73bfcd643a967e521c6c1501f66","observation_id":"f62da919-c01d-42a5-8d1e-d9a0775df70d","resolution":{"observed_at":"2026-05-11T19:31:08.932019Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2605.07020","last_updated":"2026-05-07T23:04:24Z","snapshot_observed_at":"2026-08-14T23:35:02.338673Z","submitted_at":"2026-05-07T23:04:24Z","title":"FlashMol: High-Quality Molecule Generation in as Few as Four Steps","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-11T01:01:39.724352Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2605.07020"},"observation_digest":"sha256:fcaa72d7ca0673724bedce288ea8b4bc949a1a07fbc1d04d48f9ea72096d8cbf","observation_id":"6e6e9266-f30d-45cc-9e3a-49d542b9e772","resolution":{"observed_at":"2026-05-11T04:50:57.725536Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2605.08404","last_updated":"2026-05-08T19:10:30Z","snapshot_observed_at":"2026-08-11T07:05:43.616019Z","submitted_at":"2026-05-08T19:10:30Z","title":"Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models","version":1},"reference_index":108,"source":"arxiv_source","source_observed_at":"2026-05-12T01:10:24.579325Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2605.08404"},"observation_digest":"sha256:c91d587f79c3fb591c7cae3d831ee784c7df632576c6a0b34fca3c6e8d15ad0d","observation_id":"69ba0ff5-3fee-4524-b27a-5bfb1f86fe2f","resolution":{"observed_at":"2026-05-12T08:26:24.307304Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2605.26540","last_updated":"2026-08-06T10:54:40Z","snapshot_observed_at":"2026-08-14T12:33:19.230002Z","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":8,"source":"pdf_text","source_observed_at":"2026-07-01T16:52:26.323828Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2605.26540"},"observation_digest":"sha256:456b3ee4c65e19bcaebb724d2269ab7f0052367a93e87578001097f76639fb0e","observation_id":"6deed3d3-261f-4670-8cde-e2dcab28e4c2","resolution":{"observed_at":"2026-07-01T16:55:50.503215Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.00295","last_updated":"2026-05-29T19:26:53Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T19:26:53Z","title":"Adaptive Order Policies for Masked Diffusion","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-06-28T23:33:40.937370Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.00295"},"observation_digest":"sha256:e1b5b4c99b934e566ae7dac02f9bff7ba0d47915494e2d79eaa3b03aaa39dc3a","observation_id":"08b1d6fe-78d8-4f3c-856d-140c4efd967d","resolution":{"observed_at":"2026-06-28T23:42:49.839690Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.02133","last_updated":"2026-06-11T17:13:32Z","snapshot_observed_at":"2026-08-05T11:37:25.145320Z","submitted_at":"2026-06-01T11:59:46Z","title":"Variational Learning for Insertion-based Generation","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-06-28T15:23:32.821620Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.02133"},"observation_digest":"sha256:b7ccb985946f003b2efab213fa0aef11c41a2781d904a8bd5040b2c32ced1410","observation_id":"1418030e-4dd0-4688-b19f-0521e4b5749a","resolution":{"observed_at":"2026-07-01T22:26:17.760044Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.07239","last_updated":"2026-06-05T13:07:56Z","snapshot_observed_at":"2026-08-14T14:32:48.606060Z","submitted_at":"2026-06-05T13:07:56Z","title":"Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T22:52:42.933237Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.07239"},"observation_digest":"sha256:4c51b2703b0a54385abb115ceffb7d56a0ed544e2ee689d58305584bdee9089e","observation_id":"b9ccc088-cb53-4569-b981-6f67e6b50a8a","resolution":{"observed_at":"2026-07-02T16:17:09.006335Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.18570","last_updated":"2026-08-03T19:58:44Z","snapshot_observed_at":"2026-08-13T09:53:02.724358Z","submitted_at":"2026-06-17T00:42:00Z","title":"Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning","version":1},"reference_index":194,"source":"arxiv_source","source_observed_at":"2026-06-26T19:24:05.130147Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.18570"},"observation_digest":"sha256:062faa2d0301087b3ebec678f4204508980c7ae3179786291189b57adff51dd0","observation_id":"188dd33d-92f9-4a13-bc15-e6ee4c97d61a","resolution":{"observed_at":"2026-06-26T19:29:48.138729Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.20497","last_updated":"2026-06-18T17:12:48Z","snapshot_observed_at":"2026-08-13T11:19:18.919670Z","submitted_at":"2026-06-18T17:12:48Z","title":"Interpretable Meta-Learning for Multi-Objective Chemical Search","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T15:08:37.285590Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.20497"},"observation_digest":"sha256:9e97f7e7c88c4c38c91e11b294d528f7beb9fa0b1c2d3dd2634a0dc207ab945d","observation_id":"97413a29-d3ad-4f59-85d5-bd7f2b88787f","resolution":{"observed_at":"2026-07-04T05:59:37.184715Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.22702","last_updated":"2026-06-21T22:38:35Z","snapshot_observed_at":"2026-08-15T00:02:46.580692Z","submitted_at":"2026-06-21T22:38:35Z","title":"Modular Diffusion Models for Structured Visual Recognition","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T10:29:04.711627Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.22702"},"observation_digest":"sha256:17e99552ee570741b2b49cb78f73bad2dc2a04f478cd0b8bf3ad6a1328e6aafe","observation_id":"9934d899-3ce2-48f6-be6d-1a311db9196d","resolution":{"observed_at":"2026-07-04T09:09:43.118769Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2606.28225","last_updated":"2026-07-08T23:17:43Z","snapshot_observed_at":"2026-08-12T12:19:11.289481Z","submitted_at":"2026-06-26T16:13:48Z","title":"Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs","version":1},"reference_index":257,"source":"arxiv_source","source_observed_at":"2026-06-29T04:20:15.198382Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2606.28225"},"observation_digest":"sha256:a4ae82e78bba4cd9fd5b1a1da4c7ec8b017f4d11e3bd67c41b9d3cbef7bebebe","observation_id":"39f5e676-180f-4e38-8251-c6cb0ddeeb3c","resolution":{"observed_at":"2026-07-01T17:05:50.454437Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2607.00773","last_updated":"2026-07-01T10:59:33Z","snapshot_observed_at":"2026-08-03T01:44:22.154158Z","submitted_at":"2026-07-01T10:59:33Z","title":"Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-02T16:09:39.379077Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.00773"},"observation_digest":"sha256:771578936bbee8a2e9bf741850d327d3e6dbbfaf01b45b5ef2679452b7b9ee12","observation_id":"c0b130f7-6add-436a-aae2-742dcc7a86b7","resolution":{"observed_at":"2026-07-02T16:17:08.534161Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2607.01775","last_updated":"2026-07-02T06:45:43Z","snapshot_observed_at":"2026-08-05T08:13:48.395073Z","submitted_at":"2026-07-02T06:45:43Z","title":"Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-07-03T17:30:39.458521Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.01775"},"observation_digest":"sha256:e45ccb4bd05e36d390b312c2b0afbf537e99b8632eec1ffaa2c2cba259f3b137","observation_id":"d5843b15-732a-4624-8f0c-7de38d4a1c6d","resolution":{"observed_at":"2026-07-03T17:38:43.640942Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-07-12T10:07:01.444366Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.02573","last_updated":"2026-06-30T11:12:12Z","snapshot_observed_at":"2026-08-20T10:42:41.682511Z","submitted_at":"2026-06-30T11:12:12Z","title":"Symmetry-Structured Neural Completion of Islamic Geometric Patterns from Sparse Control Geometry","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-12T10:07:01.444366Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.02573"},"observation_digest":"sha256:16aa67c7e47ce4086507fd9f4c3cb357994ce94c35024ede4df08cf82407ffc1","observation_id":"f59a465f-004f-433f-a072-75212c4f6538","resolution":{"observed_at":"2026-07-12T10:07:01.444366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":"2209.14734","doi":"10.48550/arxiv.2209.14734","metadata_source":"pith","pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Digress: Discrete denoising diffusion for graph generation","venue":"cs.LG","work_id":"b2f46572-4679-44b3-bce2-8b6a25dc278d","year":2022},"citing_paper":{"arxiv_id":"2607.06546","last_updated":"2026-07-07T17:52:17Z","snapshot_observed_at":"2026-08-15T01:01:27.416833Z","submitted_at":"2026-07-07T17:52:17Z","title":"Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-11T00:04:33.871925Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.06546"},"observation_digest":"sha256:0b308c5323c57794023bf1167ff7e094b07b7213e521b8cb2ee0f38fff41906a","observation_id":"551ab440-8e92-47dd-a7ae-3e4a1045a6bd","resolution":{"observed_at":"2026-07-11T00:07:41.986049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-02T06:19:08.446321Z","title":"Digress: Discrete denoising diffusion for graph generation.arXiv preprint arXiv:2209.14734, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.13120","last_updated":"2026-07-14T16:07:23Z","snapshot_observed_at":"2026-08-08T00:29:35.885651Z","submitted_at":"2026-07-14T16:07:23Z","title":"CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T06:19:08.446321Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.13120"},"observation_digest":"sha256:ec8927ae52b127cecf0431dfabd449940363013b813a61c35748444c2613c2b1","observation_id":"0690c03d-913e-4586-9563-f786392caaf5","resolution":{"observed_at":"2026-08-02T06:19:08.446321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14734","snapshot_observed_at":"2026-08-01T20:54:16.270318Z","title":"arXiv preprint arXiv:2209.14734 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16528","last_updated":"2026-07-17T22:09:01Z","snapshot_observed_at":"2026-08-18T01:38:13.939880Z","submitted_at":"2026-07-17T22:09:01Z","title":"Hierarchical Domain Generalization","version":1},"reference_index":146,"source":"arxiv_source","source_observed_at":"2026-08-01T20:54:16.270318Z"},"links":{"cited_paper":"/paper/2209.14734","citing_paper":"/paper/2607.16528"},"observation_digest":"sha256:78c4785f18afddb7e20a839f3d81956aada6668b53fe497cf285213c9bb98f3c","observation_id":"b37f7027-5952-49e4-91b2-2750b5af88e3","resolution":{"observed_at":"2026-08-01T20:54:16.270318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2209.14734/citation-record","integrity":"/paper/2209.14734/integrity","json":"/paper/2209.14734/citation-record.json","paper":"/paper/2209.14734"},"outbound":[],"paper":{"arxiv_id":"2209.14734","last_updated":"2023-05-23T10:32:08Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T09:43:26.374154Z","submitted_at":"2022-09-29T12:55:03Z","title":"DiGress: Discrete Denoising diffusion for graph 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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 58 inbound Pith citation observations for arXiv:2209.14734."}