{"as_of":"2026-08-11T04:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c2963013ba5b8ead30b489128f7f600c91b34f591d46596ac50a339e1a23e4a0","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T19:35:38.775572Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.00250/citation-record","integrity":"/paper/2605.00250/integrity","json":"/paper/2605.00250/citation-record.json","paper":"/paper/2605.00250"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Information geometry and its applications: Survey","venue":null,"work_id":"6cf7bc8e-9d10-4e9a-b2fb-b164099b3ba1","year":2013},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:e11590e85aa3f918ea3fe16c1404d9323cc66c64ff34514cf2a834c90078cb20","observation_id":"560b1df9-e75f-46bd-98c8-786478cbdffb","resolution":{"observed_at":"2026-05-24T22:36:26.489066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"feb3f5b8-a7d7-4285-9416-c183a89e0574","year":1982},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:f42e0a78933205adefb1ddf3bbe0d2ea0f6eaef08460d2ce116f6001de2ec0c8","observation_id":"78e905b1-c8b3-42d5-a597-57e4836e6299","resolution":{"observed_at":"2026-05-24T22:36:26.472470Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7e3c0985-562f-4ec9-8c21-ea12df4e97bb","year":1998},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:ee6f1a97f90736fa7f95f8d74269e9ce1e14584e411ace2924bad40ab22e4327","observation_id":"0a5f8666-74b6-47e2-949f-471586e5a46e","resolution":{"observed_at":"2026-05-24T22:36:26.486681Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models","venue":null,"work_id":"c2dd6b83-c711-45f3-bc81-52ad2d24f0e0","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:cbf7aaac186ec5031186e0eeaf713f1fe223695c0a6149e54660a26fe85f62e5","observation_id":"35985f9c-419d-4ddd-b317-aaf85971ab82","resolution":{"observed_at":"2026-05-24T22:36:26.483460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Equiv- ariant energy-guided SDE for inverse molecular design","venue":null,"work_id":"85707d35-9d8a-4cce-9a76-fd465580100d","year":2023},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:6c8f43f8a9a6186d6350ee215eaa3c3bd50fb98b8e28227258270c0a98b7c9d7","observation_id":"cc9e00ec-1628-4cf6-9205-49b9b16df5a4","resolution":{"observed_at":"2026-05-24T22:36:26.449924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"N.Statistical decision rules and optimal infer- ence","venue":null,"work_id":"31d5b30d-bf9e-43ef-8162-42f0da4135a6","year":2000},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:c0eaf97b5de18d00a1c842dd3fb79d86ad64f00ea09f5e8bc52f4b2c688f7c57","observation_id":"8fa2aa9c-89b4-4af0-b5a2-62ff5680ccf7","resolution":{"observed_at":"2026-05-24T22:36:26.492175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the trajectory regularity of ODE-based diffusion sampling","venue":null,"work_id":"935cc002-172e-4304-b439-a09b16e80d66","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:823d03bfd0079b4313e00956b9209b6ab6cbe038f114b9e83b986ef27ce0cda4","observation_id":"cf82fd58-d286-4423-a741-7cbd685a14ff","resolution":{"observed_at":"2026-05-24T22:36:26.459493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"aa374708-6434-4fa7-bb92-e3631059053f","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:b66242789192c6bc0e8aa9637f40c8ae32d8c512de0e769852d5be5e08b76199","observation_id":"cd7cdb4d-b70f-44ae-9f36-c700d4486b5c","resolution":{"observed_at":"2026-05-24T22:36:26.463405Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and De Grave, K","venue":null,"work_id":"92764ec4-2e95-453d-a3ee-7b1c84fb43c6","year":2010},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:c97d034f0105c2e66e61a414fa14d90dea2f596be0a377774a3039fa4b43443c","observation_id":"541a8238-2011-49d1-9c78-4fbd822fc595","resolution":{"observed_at":"2026-05-24T22:36:26.509425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Calderhead, B","venue":null,"work_id":"51a6e286-5edc-4aec-b808-e31fd5b8ade9","year":2011},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:2676f43ab7de06d0056eede319fe329f171cf166a3aeb429f9b20687c42f81b3","observation_id":"d5acd742-c8eb-42be-8e8d-1b091b08e35d","resolution":{"observed_at":"2026-05-24T22:36:26.499895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graphite: Iterative generative modeling of graphs","venue":null,"work_id":"01a7d2ef-1b9c-495a-8d2e-cedb625951a8","year":2019},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:68d1e200b887a50d0b0487c91909f87135437f8255076c83c57d1178552e6b85","observation_id":"509d0aa6-f391-4762-bfd5-5bcb812af39b","resolution":{"observed_at":"2026-05-24T22:36:26.495539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Denoising diffusion proba- bilistic models","venue":null,"work_id":"a4f5a0cc-7091-4c5d-9cb6-3afb46c6ddcf","year":2020},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:9e512a42b94cc2c7ed76de9c66507b3acea1dbafd59b48f654776f69657339bc","observation_id":"c46ef1ad-4576-4284-b82b-5541c2301632","resolution":{"observed_at":"2026-05-24T22:36:26.446089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"W., Laskey, K","venue":null,"work_id":"4cefc4fd-0990-41e9-a4f2-5e57dee99d9e","year":1983},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:ed41f218e62c119fe278e7bdb3f528816772888bb164ae5755e23bc85ddfa807","observation_id":"b60c8268-5603-40a9-bf87-199d330b67ec","resolution":{"observed_at":"2026-05-24T22:36:26.395989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"G., Vignac, C., and Welling, M","venue":null,"work_id":"fbd4d552-3a72-4179-9cb4-3ed11be3737b","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:0685a360bec5efd85f1c40f5226039a5e1500f9f44065bcf4131aa60a540474a","observation_id":"b8db4d64-8b1c-48b3-ac75-6dfe7e1239c9","resolution":{"observed_at":"2026-05-24T22:36:26.461461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"J., Sterling, T., Mysinger, M","venue":null,"work_id":"ae9c04d0-97f8-4789-bd10-79d426095e93","year":2012},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:424950c690ba7cc5d79c246f1b7d711276bb47c7f8208ca21aa86d8ab09c94a3","observation_id":"e00dfcc1-c247-4575-8385-7a46b575f71f","resolution":{"observed_at":"2026-05-24T22:36:26.445785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4408ccc1-2aaa-40c8-9489-233ee7d8b0dd","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:c1299ff1df3be23ccd93ca6b54d1a8f86a6dfcdbcd5593e2c236f6c0bc0d6ed1","observation_id":"8f66f1db-1060-4439-a4c9-5dbb5a35c1ae","resolution":{"observed_at":"2026-05-24T22:36:26.433757Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0d5cfff4-ba47-4930-8ed2-9c643065f80c","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:b891c39f4b31b41ccbc2bfadede57f92a9fbd73d5f9e04b27e59cd334bec1dfc","observation_id":"46c325f7-08a9-4eef-ba48-24038bac0980","resolution":{"observed_at":"2026-05-24T22:36:26.480809Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"707675b0-e29d-4e28-8c74-92545ce9809e","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:bbff35c09c4c57d7b727ace30ac41bc9cf2986cfeb3d4188dfc030370c712a20","observation_id":"06271298-4c2b-4831-b54e-7386152674f7","resolution":{"observed_at":"2026-05-24T22:36:26.453936Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":"739497b0-9170-4e74-88b6-3a14bd6f05b3","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:0f53b57cb897e864d7ce352ca3a4a969022ff86f169838f2f4f488f4435c5a00","observation_id":"3729954d-07eb-4784-99eb-d2d22dd3ae01","resolution":{"observed_at":"2026-05-24T22:36:26.436892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ye, J","venue":null,"work_id":"af4007d3-9fad-46a9-b45a-c700125c18d9","year":2023},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:9fad70a8c35f7cc00b0faa0d6907fe083749099f3441a24e8b62f6f0ee1249c5","observation_id":"c96c8719-dab4-42ef-9219-c8eff2bb2073","resolution":{"observed_at":"2026-05-24T22:36:26.434328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d3f14297-a85c-46e7-b309-8dc6da35893f","year":2017},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:6945da724f6a60c69b4ae2a1a9effc50f9531ddf8f4282b7e1cfcafca4df5f01","observation_id":"ccc454c5-f28a-4c4b-bd08-8855abef1398","resolution":{"observed_at":"2026-05-24T22:36:26.411379Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ping, W","venue":null,"work_id":"70588070-f45b-4dff-97e5-993a35ebb337","year":2021},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:97f25a6038d1ec798a8c606a44ecd2ce5242dbc7459c64ce13521ad7c49e390e","observation_id":"1c1cb1fb-f5f8-4e47-8b74-994d070e0c5b","resolution":{"observed_at":"2026-05-24T22:36:26.468987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"382f4824-914d-4cd2-9a61-d6d7e67f4f24","year":1908},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:7d4884e18007b5ed911c47c57749f9dd79cc4f806a0ecda98204ddfcbbb74d8b","observation_id":"ad3dae46-f2ab-40db-a03a-642996b6d95a","resolution":{"observed_at":"2026-05-24T22:36:26.418029Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Crafting papers on machine learning","venue":null,"work_id":"aeb55473-0e95-4eec-960e-6735f24809d8","year":2000},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:a2425f4f404c31bc3b050339bd52943989054584ad5ed99b95e9a1f4011ad16c","observation_id":"7241ad5f-73a5-4b62-ab8c-e04e1aec0651","resolution":{"observed_at":"2026-05-24T22:36:26.431176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Alleviating exposure bias in diffusion models through sampling with shifted time steps","venue":null,"work_id":"f44a83cd-ca34-4248-b507-d8c0ef2a4ab2","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:dd42c577ae9246cbbf322dff10bae88e7bb4398756293336bc942640e84433f5","observation_id":"9312501c-7006-4643-9b78-712cf4e67937","resolution":{"observed_at":"2026-05-24T22:36:26.408072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fisher-Rao metric, geometry, and complexity of neural networks","venue":null,"work_id":"007823ae-2a37-47e1-be1f-d62c2f274a3c","year":2019},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:d4d3cb5749405b0b759e4eb7cd09222c5d9a7436d85e84d935b2792a9faa9d53","observation_id":"159cb2ab-ad6a-438c-8707-5dc72d3182ec","resolution":{"observed_at":"2026-05-24T22:36:26.414848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"I 2SB: Image-to-image Schr¨odinger bridge","venue":null,"work_id":"23384dbb-797a-4433-8197-e86edef6dd94","year":2023},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:07b5803726b17fc54195eb7f3cf782b55092dac11d0b772d125062db9f845d47","observation_id":"3e1eb128-2c20-400a-a860-70adaf2ef04e","resolution":{"observed_at":"2026-05-24T22:36:26.464838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DPM- solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","venue":null,"work_id":"1db4a45f-8f9a-48c1-9c78-88b4e8aff8c2","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:4904f05d046fcbd6425c327d7232634f9794e8c1622013bc8b87ff6324f1056f","observation_id":"1ae3cd4e-454f-4c4e-af86-14b3ab674e6f","resolution":{"observed_at":"2026-05-24T22:36:26.408427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GraphDF: A discrete flow model for molecular graph generation","venue":null,"work_id":"5b1058ed-a96e-4e1b-ae28-73ada4b27c3f","year":2021},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:5b2a17a4611021e5f1431c264c169cfe2c7dee1f69f1e5c7c078de4794fc8792","observation_id":"1a4141f3-a85c-45f3-bfe6-6d18f7acc57a","resolution":{"observed_at":"2026-05-24T22:36:26.436569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators","venue":null,"work_id":"3e5c1f51-40de-440f-b7cc-f33816e8d0ea","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:527e726b882ff1ebe01151ae84da2abb8602d15487744514fe4cc6c0285769e1","observation_id":"3ce0a944-d032-4950-ba3b-1028d013c1bc","resolution":{"observed_at":"2026-05-24T22:36:26.442565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Continuous markov processes and stochastic equations.Rendiconti del Circolo Matematico di Palermo, 4(1):48–90","venue":null,"work_id":"c602e928-053a-456d-b195-64032b1ed9ac","year":1955},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:081e3d465951a4eeb32053427a1c86074dd7e880b2bb8732e59913458608499c","observation_id":"78799355-75c2-4bff-9d73-52db4e01de7c","resolution":{"observed_at":"2026-05-24T22:36:26.440039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Constraint Ornstein-Uhlenbeck bridges.Jour- nal of Mathematical Physics, 58(9)","venue":null,"work_id":"b5b8cd11-2abf-41a5-a762-17deb143b0f3","year":2017},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:a936831930516b8902864d5141b7780cc373b2517ab417648217314f45c72667","observation_id":"97554059-bf3d-410a-8b04-66e3a483a71e","resolution":{"observed_at":"2026-05-24T22:36:26.365480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Permutation invariant graph generation via score-based generative modeling","venue":null,"work_id":"b87094c8-c80d-4ebf-942e-2f6ad3e9d240","year":2020},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:680a18a906be99570e44148c8a0d18bc3a7d50bad2c86a516f7f678e3421e253","observation_id":"30ee233a-e8dd-4589-ac84-b20f6fc68b5e","resolution":{"observed_at":"2026-05-24T22:36:26.421588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Jump your steps: Optimizing sampling schedule of discrete diffusion models","venue":null,"work_id":"0e0712f9-b912-4a62-9b6e-de66981822c0","year":2025},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:3984c2f585b893e366e8b6a34cd031416ca7fd389958a4f8dcf675f940e93280","observation_id":"1a3c101e-9f6c-432d-98b8-daeca27fec4b","resolution":{"observed_at":"2026-05-24T22:36:26.499110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fr ´echet chemnet distance: a metric for generative models for molecules in drug discovery.Jour- nal of Chemical Information and Modeling, 58(9):1736– 1741","venue":null,"work_id":"7d12f727-e427-4ad0-928e-c88cab1430a5","year":2018},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:8e4e90523043ad2da7047acfa9630ceb0c4e8ca98a6879972fbbee69b0f3f778","observation_id":"c756af4e-afe5-4833-b481-48e57c80d3fc","resolution":{"observed_at":"2026-05-24T22:36:26.414519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DeFoG: Discrete flow matching for graph generation","venue":null,"work_id":"3af9dc0e-e03e-48c9-b663-bc60bc97c787","year":2025},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:c60e05798b060c9905cb225150ccf75339d5ba6371f2428fabacf23f774ed70f","observation_id":"01cf1373-538b-4e5c-bba8-a3dfe0d819ef","resolution":{"observed_at":"2026-05-24T22:36:26.390905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"O., Rupp, M., and V on Lilienfeld, O","venue":null,"work_id":"eb2ff781-9229-482c-93f9-2138d370421e","year":2014},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:77dfb98561e7ea24ee5f7b8930e10203d0d0c6afdfddd8db091b37855f3b04c7","observation_id":"df8bf507-8f2d-4067-a179-af4193b16b63","resolution":{"observed_at":"2026-05-24T22:36:26.393555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Align your steps: Optimizing sampling schedules in diffusion models","venue":null,"work_id":"8b92853b-bce1-40de-bf7e-8c3fa862d483","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:3e603ea5017e02407fb7997cc4c84355cf1bc8d6d9390e40ccf824a0e2215dd6","observation_id":"004586c2-06f8-48ce-a413-beeba32ffd35","resolution":{"observed_at":"2026-05-24T22:36:26.427888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Collective classification in network data.AI Magazine, 29(3):93–93","venue":null,"work_id":"9e3817e8-e71b-4a79-9356-0bfec12b1d3e","year":2008},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:9c51fb0fe04b70a7a15e285a420dff0d995f768eedd0bcfb71b0b2708ae4360f","observation_id":"503a014f-f13b-4e5a-a63b-ffd440540024","resolution":{"observed_at":"2026-05-24T22:36:26.375957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GraphAF: a flow-based autoregressive model for molecu- lar graph generation","venue":null,"work_id":"ea858eb3-c2c3-4b04-9502-ab5c60dd3898","year":2020},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:ab07becc0402d1fefe8f1b138db8374dce898211b9f5267e850dbbd370e7f9ad","observation_id":"5be30a5f-fe3f-4643-87bb-c9d6bc3d4d39","resolution":{"observed_at":"2026-05-24T22:36:26.405396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18252","last_updated":"2024-11-12T08:01:04Z","snapshot_observed_at":"2026-07-06T18:06:44.995721Z","submitted_at":"2024-04-28T17:18:41Z","title":"Improving Training-free Conditional Diffusion Model via Fisher Information","version":2},"cited_work":{"arxiv_id":"2404.18252","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.18252","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Lai, H","venue":null,"work_id":"3c6294b6-e7b5-4b8f-ba43-96d5abf9c517","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"cited_paper":"/paper/2404.18252","citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:f0785abd84aa2f0f2a7ebfe2240fee5983f3139a868a12f181643a04ed3d2fb3","observation_id":"f9bedf94-7e61-4d6b-a04f-92ecf651d422","resolution":{"observed_at":"2026-05-11T15:36:08.122049Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ermon, S","venue":null,"work_id":"8423243f-eded-4b49-96c3-90bb2b6f5c02","year":2019},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:9075d22a658903bb7743d71753f51bf31acb547b7ccad7fae210a81d8f38a97d","observation_id":"f66ccb8e-03fd-42df-8454-6759758b3cca","resolution":{"observed_at":"2026-05-24T22:36:26.369807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"V ., and Niepert, M","venue":null,"work_id":"a54409c4-81e3-4560-8c77-6aac1ec1de52","year":2025},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:0f543b2560f59f4e8861c5c0af1c289c435fdad034a6bfd7622708e69f25625a","observation_id":"61c02c7c-8604-4a4b-95d8-db012723a24f","resolution":{"observed_at":"2026-05-24T22:36:26.368891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ccda0f8a-dc27-4f7c-8113-c7b3b5b9b94a","year":1930},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:421bb39842f4623e17acb3788f1102e68b45484e5ff42d92f08d9869a91d0c25","observation_id":"b37415f8-8649-4552-a889-9a5125005a96","resolution":{"observed_at":"2026-05-24T22:36:26.392974Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DiGress: Discrete denoising diffusion for graph generation","venue":null,"work_id":"890624f5-cabb-4363-87fd-1083e50bff70","year":2023},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:a75d31e06c8a1d49ed766ebda83b0d48c8ef32cd4cd0c958b29eb89a456173ce","observation_id":"4f4e8e78-e6f7-45a0-b046-c95c89617936","resolution":{"observed_at":"2026-05-24T22:36:26.390420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning fast samplers for diffusion models by differentiating through sample quality","venue":null,"work_id":"c80a27a5-dc40-4dde-8464-f6002176fc05","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:7be3c8dfbaa14e144d84e91741da26749a744411fb36e9985f8afce197b86aba","observation_id":"c711ded8-6594-4363-8879-53661682239d","resolution":{"observed_at":"2026-05-24T22:36:26.399789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GeoDiff: A geometric diffusion model for molecular conformation generation","venue":null,"work_id":"ab6d8e6c-e786-4cbf-8063-373b3385ea85","year":2022},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:107cba43928e2742ce7032ff5a0136fd39de42287e2e830934da17d7a0a3e2e5","observation_id":"0a10241b-cb4b-4196-af1c-1313422c67f3","resolution":{"observed_at":"2026-05-24T22:36:26.378934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Accelerating diffusion sampling with optimized time steps","venue":null,"work_id":"259e1548-95c8-4de1-b4f2-d1e2d8dfabef","year":2024},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:5d2aed069a9fbbb06af2465c50d72d6cddc7edf11ba04f4e3aac255275527bb5","observation_id":"e52538d1-14f1-41ee-b3cf-ece1e9aabb5b","resolution":{"observed_at":"2026-05-24T22:36:26.458244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Chen, Y","venue":null,"work_id":"ed110a27-5fbd-4cae-b504-9516e9a86764","year":2023},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:37e88f528b2a7e8575f407462edaa0b5297e6bf919d51e7af41072040bd46186","observation_id":"507ed9f2-63d1-440a-8d5d-9256f8629278","resolution":{"observed_at":"2026-05-24T22:36:26.378167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GraphVite: A high- performance CPU-GPU hybrid system for node embed- ding","venue":null,"work_id":"fbb640ee-242d-4c71-924f-4748b2500fad","year":2019},"citing_paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-09T19:35:38.775572Z"},"links":{"citing_paper":"/paper/2605.00250"},"observation_digest":"sha256:4391e051f9683d1a697ec3864743e82ff8e466b591ba34d96301197f83ac2884","observation_id":"b23b3114-f1c5-45d9-b0ee-dab065702c67","resolution":{"observed_at":"2026-05-24T22:36:26.449344Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.00250","last_updated":"2026-04-30T21:32:35Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T21:32:35Z","title":"Information-geometric adaptive sampling for graph diffusion"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":50},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2605.00250."}