{"as_of":"2026-08-11T05:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:76da5c35f1f8db02c1b8fe8c7a84fe9bda2fef1e6b98ec51777388c2372e2ab9","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:34:14.530591Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T16:59:57.967386Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-08-10T22:34:14.530591Z","title":"Fasterdit: Towards faster diffusion transformers training without architecture modification","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01423","last_updated":"2025-03-10T11:43:42Z","snapshot_observed_at":"2026-08-11T01:06:46.210110Z","submitted_at":"2025-01-02T18:59:40Z","title":"Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T22:34:14.530591Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2501.01423"},"observation_digest":"sha256:45915ca6a507823712176be60315eef34e76029135b3cc1e97b623bd8d11a662","observation_id":"1758c7c4-738c-4b0f-8e77-346c463fd748","resolution":{"observed_at":"2026-08-10T22:34:14.530591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-08-07T11:18:59.416091Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03275","last_updated":"2025-06-03T18:03:32Z","snapshot_observed_at":"2026-08-09T22:41:58.306259Z","submitted_at":"2025-06-03T18:03:32Z","title":"Chipmunk: Training-Free Acceleration of Diffusion Transformers with Dynamic Column-Sparse Deltas","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:18:59.416091Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2506.03275"},"observation_digest":"sha256:48fb94c24b5d1f6ae0be28b5f86a62db71a8d759eb69dab3c6789a49d510e7c5","observation_id":"b9bcafd8-8fa9-443b-8de4-4132e87f51da","resolution":{"observed_at":"2026-08-07T11:18:59.416091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-08-05T05:27:34.345840Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05441","last_updated":"2025-09-10T22:15:25Z","snapshot_observed_at":"2026-08-06T13:34:56.299668Z","submitted_at":"2025-09-05T18:49:08Z","title":"Missing Fine Details in Images: Last Seen in High Frequencies","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-05T05:27:34.345840Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2509.05441"},"observation_digest":"sha256:e5474e8cbfbde494f616333a4d555297622215a1256bd8a76a0f6031af2a2153","observation_id":"01d5d6e5-e09d-49f2-9c22-4ee27fbee4ef","resolution":{"observed_at":"2026-08-05T05:27:34.345840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":"2410.10356","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-07-04T16:59:57.967386Z","title":"Fasterdit: Towards faster diffusion transformers training without architecture modification","venue":null,"work_id":"073ce999-fc96-44f1-9ec7-2e3e7517d328","year":2024},"citing_paper":{"arxiv_id":"2605.10790","last_updated":"2026-05-11T16:21:45Z","snapshot_observed_at":"2026-08-10T22:36:26.076418Z","submitted_at":"2026-05-11T16:21:45Z","title":"Elucidating Representation Degradation Problem in Diffusion Model Training","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-12T04:08:11.110912Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2605.10790"},"observation_digest":"sha256:531f3b5a6e16b50089fac37d3797219ffd3a51c679bec0fb275ab7f80c5868e4","observation_id":"961f1249-50dc-4483-99c0-a163a2b97288","resolution":{"observed_at":"2026-05-12T06:36:26.586109Z","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":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":"2410.10356","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-07-04T16:59:57.967386Z","title":"Fasterdit: Towards faster diffusion transformers training without architecture modification","venue":null,"work_id":"073ce999-fc96-44f1-9ec7-2e3e7517d328","year":2024},"citing_paper":{"arxiv_id":"2606.11096","last_updated":"2026-06-09T16:53:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-09T16:53:30Z","title":"IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-27T13:10:14.308216Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2606.11096"},"observation_digest":"sha256:1cb6be2dfd5b73472e22237503aef51cb8037429e4a0afa82b6ea796e5a00c1d","observation_id":"71145cbf-138e-4362-bb5f-bd5b55f768a1","resolution":{"observed_at":"2026-07-03T05:37:40.238986Z","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":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification","version":2},"cited_work":{"arxiv_id":"2410.10356","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.10356","snapshot_observed_at":"2026-07-04T16:59:57.967386Z","title":"Fasterdit: Towards faster diffusion transformers training without architecture modification","venue":null,"work_id":"073ce999-fc96-44f1-9ec7-2e3e7517d328","year":2024},"citing_paper":{"arxiv_id":"2606.24888","last_updated":"2026-06-23T17:59:55Z","snapshot_observed_at":"2026-08-08T16:38:03.178919Z","submitted_at":"2026-06-23T17:59:55Z","title":"DiffusionBench: On Holistic Evaluation of Diffusion Transformers","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-26T00:06:11.951205Z"},"links":{"cited_paper":"/paper/2410.10356","citing_paper":"/paper/2606.24888"},"observation_digest":"sha256:653e1866dce1b058a1a9a7f98a2789a454405b8ee4cd4bc022253047aef11d4f","observation_id":"e0019a56-ce27-43a2-9762-783924a8ae32","resolution":{"observed_at":"2026-07-04T16:59:57.970129Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2410.10356/citation-record","integrity":"/paper/2410.10356/integrity","json":"/paper/2410.10356/citation-record.json","paper":"/paper/2410.10356"},"outbound":[],"paper":{"arxiv_id":"2410.10356","last_updated":"2024-10-31T12:49:09Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T14:19:16.047359Z","submitted_at":"2024-10-14T10:17:24Z","title":"FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification"},"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":"2026-08-10T06:31:04.303077+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 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.10356."}