{"as_of":"2026-08-19T10:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7e6c5dad49b20540b38fb2a4f0652fa49d6c2d7cd30eface8455d33f09ff4fef","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":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T21:47:19.989848Z","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":"2026-06-30T23:45:07.785757Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2510.02283","last_updated":"2025-10-02T17:55:42Z","snapshot_observed_at":"2026-08-13T04:00:21.942422Z","submitted_at":"2025-10-02T17:55:42Z","title":"Self-Forcing++: Towards Minute-Scale High-Quality Video Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-15T22:39:53.995700Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2510.02283"},"observation_digest":"sha256:961cfe16aa0be8c5ec62a5c6ff0b0095bb5dc42b75922bee2ed925cadb6f5c76","observation_id":"b7c35c85-f56b-437b-83d0-7fd1261aa166","resolution":{"observed_at":"2026-05-15T22:39:54.155012Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-08-03T21:47:19.989848Z","title":"Learning few-step diffusion models by trajectory distribution matching.arXiv preprint arXiv:2503.06674,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.13649","last_updated":"2026-07-03T19:09:38Z","snapshot_observed_at":"2026-08-16T18:41:17.237259Z","submitted_at":"2025-11-17T17:59:54Z","title":"Distribution Matching Distillation Meets Reinforcement Learning","version":5},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T21:47:19.989848Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2511.13649"},"observation_digest":"sha256:4d092ffdbafba5da343f5e7a492d7c3ca25ec3e7d328dba7108e8f1f196fc8f7","observation_id":"51dff4a3-666f-4617-ac94-06e83d94b4ef","resolution":{"observed_at":"2026-08-03T21:47:19.989848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2512.04677","last_updated":"2026-07-31T10:43:50Z","snapshot_observed_at":"2026-08-13T11:03:16.956715Z","submitted_at":"2025-12-04T11:11:24Z","title":"Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length","version":5},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-17T01:56:44.123092Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2512.04677"},"observation_digest":"sha256:618a156961aa241a23cf3b47c483ef6a8a9ce22fdeb298682b215ec0831743db","observation_id":"b66392ac-d9b4-4750-ae0e-8eeac90f9653","resolution":{"observed_at":"2026-05-17T01:58:51.436368Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-08-03T18:39:42.201403Z","title":"Learning few-step diffusion models by trajectory distribution matching.arXiv preprint arXiv:2503.06674, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.04677","last_updated":"2026-07-31T10:43:50Z","snapshot_observed_at":"2026-08-13T11:03:16.956715Z","submitted_at":"2025-12-04T11:11:24Z","title":"Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length","version":6},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T18:39:42.201403Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2512.04677"},"observation_digest":"sha256:78a64862a3c5a5433ac8ac42d1f85b62a103ed1ee3ebd3289fee1a8e0264f6d0","observation_id":"6a612ec4-8215-42b1-8543-4df5888466b5","resolution":{"observed_at":"2026-08-03T18:39:42.201403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2512.16776","last_updated":"2025-12-18T17:08:12Z","snapshot_observed_at":"2026-08-18T19:56:03.713377Z","submitted_at":"2025-12-18T17:08:12Z","title":"Kling-Omni Technical Report","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T21:00:58.473043Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2512.16776"},"observation_digest":"sha256:4d7e3e431230b2ba5bc57571682e202ec16f0dda99997b46cb386f401767c04e","observation_id":"de8bf78d-5317-497e-a034-ba0ea9dcd6ea","resolution":{"observed_at":"2026-05-15T21:00:58.622991Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2602.03139","last_updated":"2026-05-19T12:34:41Z","snapshot_observed_at":"2026-08-14T18:45:39.954413Z","submitted_at":"2026-02-03T05:45:25Z","title":"Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-21T14:37:09.487669Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2602.03139"},"observation_digest":"sha256:23d473d8f62f9e9492befa74f8b3848edbf4e1c2de6841b6510a256d201866e2","observation_id":"d79df449-9464-4e6b-8516-1dc89d4c9bda","resolution":{"observed_at":"2026-05-21T14:40:14.496146Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-07-15T14:00:06.859472Z","title":"arXiv preprint arXiv:2503.06674 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.06136","last_updated":"2026-06-30T13:32:05Z","snapshot_observed_at":"2026-08-18T22:48:47.027264Z","submitted_at":"2026-03-06T10:45:07Z","title":"Cross-Resolution Distribution Matching for Diffusion Distillation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-15T14:00:06.859472Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2603.06136"},"observation_digest":"sha256:8686031dab560bda428be728eb39521bc69f1c364fe9e7d5dc68b55ad34df138","observation_id":"0a500951-cf31-4802-ac95-4b846d2316a3","resolution":{"observed_at":"2026-07-15T14:00:06.859472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2604.04018","last_updated":"2026-04-05T08:30:35Z","snapshot_observed_at":"2026-08-17T17:35:39.490737Z","submitted_at":"2026-04-05T08:30:35Z","title":"1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-13T17:19:49.891090Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2604.04018"},"observation_digest":"sha256:e5b195638c1aebe933784ad42c1a9cffc5435e1a962729b378c78b1815f039cc","observation_id":"5fee760f-f85c-4b33-a748-1610b5948771","resolution":{"observed_at":"2026-05-13T17:23:02.581307Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2604.14580","last_updated":"2026-05-05T22:56:58Z","snapshot_observed_at":"2026-08-14T00:06:57.661654Z","submitted_at":"2026-04-16T03:19:29Z","title":"TurboTalk: Progressive Distillation for One-Step Audio-Driven Talking Avatar Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T11:13:27.689539Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2604.14580"},"observation_digest":"sha256:23923b3428f5fcf9e79ee22b8cf889b895ba5d66575d033a143a431d7b3775bf","observation_id":"61b4a80e-a1e5-47c4-a9b9-640103d19519","resolution":{"observed_at":"2026-05-10T11:15:10.356621Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2604.15911","last_updated":"2026-04-17T10:11:39Z","snapshot_observed_at":"2026-08-11T06:56:48.614928Z","submitted_at":"2026-04-17T10:11:39Z","title":"Efficient Video Diffusion Models: Advancements and Challenges","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-05-10T08:28:29.706249Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2604.15911"},"observation_digest":"sha256:df871d5bc90a1442df50a495ea8cd20ff565b1adb83c0aed362fd25c4f853c3b","observation_id":"cac9cbc2-50fa-4c77-8bea-4e857f02dfdd","resolution":{"observed_at":"2026-05-10T09:03:25.737707Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2604.23632","last_updated":"2026-04-26T09:46:58Z","snapshot_observed_at":"2026-07-06T23:09:48.137856Z","submitted_at":"2026-04-26T09:46:58Z","title":"Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-08T06:56:19.795651Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2604.23632"},"observation_digest":"sha256:d3f290196f37ce90a28ef3bb36704a5d16e5806d49d075d5680c941a75f0a776","observation_id":"bb3f739b-2427-418e-8f07-4601aeaaf72e","resolution":{"observed_at":"2026-05-11T21:06:12.754600Z","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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2605.05204","last_updated":"2026-05-26T15:05:04Z","snapshot_observed_at":"2026-08-03T09:59:43.729293Z","submitted_at":"2026-05-06T17:59:34Z","title":"D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-08T17:25:26.391582Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:5a27196b2bafecd63fa3121320a2e3befc41a5ebd195e8a1c595822b77737361","observation_id":"57555084-1a72-4313-91e9-84a9aa2318d7","resolution":{"observed_at":"2026-05-11T17:36:05.737231Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2605.05204","last_updated":"2026-05-26T15:05:04Z","snapshot_observed_at":"2026-08-03T09:59:43.729293Z","submitted_at":"2026-05-06T17:59:34Z","title":"D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-20T23:18:35.390642Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:cc88186264a51ab87d6f6a32ddd82c87d70e50d0958d1abcb20d5744d45d9c93","observation_id":"09fcae9f-02de-401d-aaab-c041a536e820","resolution":{"observed_at":"2026-05-20T23:19:13.359978Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2605.05204","last_updated":"2026-05-26T15:05:04Z","snapshot_observed_at":"2026-08-03T09:59:43.729293Z","submitted_at":"2026-05-06T17:59:34Z","title":"D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-30T23:44:10.302520Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:f6f20db8c68119bd67e687e7c4491082b7b8cf602c0d636e5a79719217c0dac8","observation_id":"a46bc800-0994-4bc4-bc2f-f180ab6b5d06","resolution":{"observed_at":"2026-06-30T23:45:07.787257Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2503.06674","last_updated":"2025-03-12T12:25:18Z","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching","version":2},"cited_work":{"arxiv_id":"2503.06674","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.06674","snapshot_observed_at":"2026-06-30T23:45:07.785757Z","title":"Learning few- step diffusion models by trajectory distribution matching","venue":null,"work_id":"9a0f72fb-aad5-4746-94ea-ee9f2f29d8b0","year":2025},"citing_paper":{"arxiv_id":"2605.17834","last_updated":"2026-05-18T04:16:15Z","snapshot_observed_at":"2026-08-13T07:11:22.447969Z","submitted_at":"2026-05-18T04:16:15Z","title":"Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-20T12:15:19.371127Z"},"links":{"cited_paper":"/paper/2503.06674","citing_paper":"/paper/2605.17834"},"observation_digest":"sha256:65aa904dca705213685ed6769f36d37ac51edc3874c8b824232a325df8f5585b","observation_id":"413ce80c-ddf8-420d-bc0d-00db06837013","resolution":{"observed_at":"2026-05-20T12:18:16.644120Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2503.06674/citation-record","integrity":"/paper/2503.06674/integrity","json":"/paper/2503.06674/citation-record.json","paper":"/paper/2503.06674"},"outbound":[],"paper":{"arxiv_id":"2503.06674","last_updated":"2025-03-12T12:25:18Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T12:51:46.067968Z","submitted_at":"2025-03-09T15:53:49Z","title":"Learning Few-Step Diffusion Models by Trajectory Distribution Matching"},"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 15 inbound Pith citation observations for arXiv:2503.06674."}