{"as_of":"2026-08-19T20:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a32477bee5fa31c44faa6597938f629871587ed5b0111e5e410595eb14aee672","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T11:36:01.422555Z","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":"2026-07-01T15:45:48.747982Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-08-11T11:36:01.422555Z","title":"Q-diffusion: Quantizing diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15341","last_updated":"2025-03-11T20:52:10Z","snapshot_observed_at":"2026-08-11T18:47:41.328896Z","submitted_at":"2024-12-19T19:13:18Z","title":"Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T11:36:01.422555Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2412.15341"},"observation_digest":"sha256:ffad4d8c9d7bf32791695399701d491ce65438e7d0a71ffb83c7aca827ffc6a8","observation_id":"49a99859-5dbb-47fd-94b4-2602fc404253","resolution":{"observed_at":"2026-08-11T11:36:01.422555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-08-09T19:05:52.899605Z","title":", Liu, Y","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00433","last_updated":"2025-02-01T13:46:02Z","snapshot_observed_at":"2026-08-15T08:08:52.669892Z","submitted_at":"2025-02-01T13:46:02Z","title":"CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T19:05:52.899605Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2502.00433"},"observation_digest":"sha256:dcfc5bce771a72b9a8ae2c4c45b5e14a8a16dd2cb2ef5c643bbebf9b12015d81","observation_id":"c36eb70a-11f2-47ef-8c6e-aa137c79ec0c","resolution":{"observed_at":"2026-08-09T19:05:52.899605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-08-05T17:31:13.046427Z","title":"arXiv preprint arXiv:2302.04304","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.16211","last_updated":"2025-08-22T08:34:03Z","snapshot_observed_at":"2026-08-17T04:26:24.384790Z","submitted_at":"2025-08-22T08:34:03Z","title":"Forecast then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T17:31:13.046427Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2508.16211"},"observation_digest":"sha256:ef6474828dd8c191fa5045ddbaa8f8bfd2d5dace415f38683e5f6f8cc30363f3","observation_id":"d95f1001-e8a8-4adc-b50f-b96ef7d1a462","resolution":{"observed_at":"2026-08-05T17:31:13.046427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-08-05T12:27:44.325182Z","title":"Q-diffusion: Quantizing diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01624","last_updated":"2025-09-01T17:09:22Z","snapshot_observed_at":"2026-08-15T13:25:08.064535Z","submitted_at":"2025-09-01T17:09:22Z","title":"Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T12:27:44.325182Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2509.01624"},"observation_digest":"sha256:b7b3976dd2eacf2b9ad7468442229cceb44939e0c8f1912257505724a5af48e3","observation_id":"c132c994-1042-473c-b8d5-7e6bccf64102","resolution":{"observed_at":"2026-08-05T12:27:44.325182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":"2302.04304","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-07-01T15:45:48.747982Z","title":"2023.Q-Diffusion: Quantizing Diffusion Models","venue":null,"work_id":"56679ba6-dc2f-4c5f-90e2-6a162ed2a70f","year":2023},"citing_paper":{"arxiv_id":"2604.06916","last_updated":"2026-04-08T10:14:47Z","snapshot_observed_at":"2026-08-11T20:38:10.898171Z","submitted_at":"2026-04-08T10:14:47Z","title":"FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T18:10:06.994557Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2604.06916"},"observation_digest":"sha256:b187832161b595095ecc05333c475016913b802ffa7d378b9e8229e5b4fb3c07","observation_id":"b0ad8cff-4b92-4077-8652-86a4fdee2655","resolution":{"observed_at":"2026-05-11T05:21:07.986625Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":"2302.04304","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-07-01T15:45:48.747982Z","title":"2023.Q-Diffusion: Quantizing Diffusion Models","venue":null,"work_id":"56679ba6-dc2f-4c5f-90e2-6a162ed2a70f","year":2023},"citing_paper":{"arxiv_id":"2604.12668","last_updated":"2026-04-14T12:39:13Z","snapshot_observed_at":"2026-08-14T03:10:16.209626Z","submitted_at":"2026-04-14T12:39:13Z","title":"OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T15:37:25.939058Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2604.12668"},"observation_digest":"sha256:d7cefc62e23cbe6f219e6acf654a639f96fedae0625a554cdc586add5548539d","observation_id":"e5da01f5-61cd-4971-b213-717808db52a7","resolution":{"observed_at":"2026-05-11T10:11:02.275990Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":"2302.04304","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-07-01T15:45:48.747982Z","title":"2023.Q-Diffusion: Quantizing Diffusion Models","venue":null,"work_id":"56679ba6-dc2f-4c5f-90e2-6a162ed2a70f","year":2023},"citing_paper":{"arxiv_id":"2604.18348","last_updated":"2026-04-20T14:43:36Z","snapshot_observed_at":"2026-08-12T15:33:21.961041Z","submitted_at":"2026-04-20T14:43:36Z","title":"AdaCluster: Adaptive Query-Key Clustering for Sparse Attention in Video Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T05:09:46.155328Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2604.18348"},"observation_digest":"sha256:8b722904f6a35e66dd53a489712e8cacb598a2c5b128d5db20b41d632874378a","observation_id":"b50a942c-e13d-44e0-b753-d53dfe48323f","resolution":{"observed_at":"2026-05-10T09:43:49.776552Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":"2302.04304","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-07-01T15:45:48.747982Z","title":"2023.Q-Diffusion: Quantizing Diffusion Models","venue":null,"work_id":"56679ba6-dc2f-4c5f-90e2-6a162ed2a70f","year":2023},"citing_paper":{"arxiv_id":"2606.28421","last_updated":"2026-07-08T14:55:43Z","snapshot_observed_at":"2026-07-12T11:46:49.191265Z","submitted_at":"2026-06-25T17:14:03Z","title":"JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T01:02:20.024793Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2606.28421"},"observation_digest":"sha256:b6114b6d35bf5b81e07eea918146f8309697d541b4503b23bd2d3351b353a412","observation_id":"3ad43853-aab0-4cb3-b341-e64000ad351f","resolution":{"observed_at":"2026-07-01T15:45:48.749505Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04304","snapshot_observed_at":"2026-07-12T11:46:50.520083Z","title":"Q-diffusion: Quantizing diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.28421","last_updated":"2026-07-08T14:55:43Z","snapshot_observed_at":"2026-07-12T11:46:49.191265Z","submitted_at":"2026-06-25T17:14:03Z","title":"JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-12T11:46:50.520083Z"},"links":{"cited_paper":"/paper/2302.04304","citing_paper":"/paper/2606.28421"},"observation_digest":"sha256:fccb5448fa06371d3f9f6efc7e2858cc6d708c9b6a1241ddb6479c63c1bfd1bd","observation_id":"3c085ed7-cc64-4866-a034-796922d3d03c","resolution":{"observed_at":"2026-07-12T11:46:50.520083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2302.04304/citation-record","integrity":"/paper/2302.04304/integrity","json":"/paper/2302.04304/citation-record.json","paper":"/paper/2302.04304"},"outbound":[],"paper":{"arxiv_id":"2302.04304","last_updated":"2023-06-08T09:21:05Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T06:46:15.035632Z","submitted_at":"2023-02-08T19:38:59Z","title":"Q-Diffusion: Quantizing Diffusion Models"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2302.04304."}