{"as_of":"2026-08-09T11:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c94c20250f60740ab8783502eb04b54575c7c7ce7bf98543a61ec06108192fbc","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T14:16:13.014126Z","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":"2026-06-30T23:45:07.775985Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-08-07T14:16:13.014126Z","title":"Tinyfusion: Diffusion transformers learned shallow.arXiv preprint arXiv:2412.01199, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19602","last_updated":"2025-05-26T07:11:42Z","snapshot_observed_at":"2026-08-08T23:51:09.753030Z","submitted_at":"2025-05-26T07:11:42Z","title":"Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:16:13.014126Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2505.19602"},"observation_digest":"sha256:4d0aed7c2def9eff6b6727546aa6da8c6691701dd93790ba3e18e6cd2264d385","observation_id":"0a678a28-5f88-44c4-8487-7a8823fc6d0f","resolution":{"observed_at":"2026-08-07T14:16:13.014126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-08-07T10:29:03.057675Z","title":"Tinyfusion: Diffusion transformers learned shallow","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05340","last_updated":"2025-06-06T17:59:47Z","snapshot_observed_at":"2026-08-08T23:57:15.811108Z","submitted_at":"2025-06-05T17:59:40Z","title":"Exploring Diffusion Transformer Designs via Grafting","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:29:03.057675Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2506.05340"},"observation_digest":"sha256:639b6b33c1cc4be85b2e0a41d20d2237f8120e4349f3abbf99585e7783c08456","observation_id":"edbe72d0-cb34-4d4e-b1b0-ac42901b1c2d","resolution":{"observed_at":"2026-08-07T10:29:03.057675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":"2412.01199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-06-30T23:45:07.775985Z","title":"arXiv preprint arXiv:2412.01199 (2024)","venue":null,"work_id":"feb9208b-f79c-480e-b6af-6d82e8a91334","year":2024},"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":19,"source":"pdf_text","source_observed_at":"2026-05-08T17:25:26.391582Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:54df6706ab155dbd00743f739e9b2fa44afb63e92a8ac46e992f041535bfad96","observation_id":"53b87ede-de37-4557-93fe-5721399bcbbb","resolution":{"observed_at":"2026-05-11T17:36:05.633793Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":"2412.01199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-06-30T23:45:07.775985Z","title":"arXiv preprint arXiv:2412.01199 (2024)","venue":null,"work_id":"feb9208b-f79c-480e-b6af-6d82e8a91334","year":2024},"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":19,"source":"pdf_text","source_observed_at":"2026-05-20T23:18:35.390642Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:3991be82a16fd3e1a35465326c741307a378868b7c24a49850715dd9e560d14c","observation_id":"5e10b71f-c495-4be8-b564-b47ed95c825c","resolution":{"observed_at":"2026-05-20T23:19:13.705869Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":"2412.01199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-06-30T23:45:07.775985Z","title":"arXiv preprint arXiv:2412.01199 (2024)","venue":null,"work_id":"feb9208b-f79c-480e-b6af-6d82e8a91334","year":2024},"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":19,"source":"pdf_text","source_observed_at":"2026-06-30T23:44:10.302520Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2605.05204"},"observation_digest":"sha256:cb41094610a7918348f7d4749c41af6cc884fe626b40ab79968c3d41ea27fc71","observation_id":"0f9f6433-35ec-46ce-98e3-aa96f9f909ac","resolution":{"observed_at":"2026-06-30T23:45:07.777380Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.01199","snapshot_observed_at":"2026-08-02T06:05:02.778859Z","title":"Tinyfu- sion: Diffusion transformers learned shallow,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.13164","last_updated":"2026-07-14T18:15:32Z","snapshot_observed_at":"2026-08-07T04:26:16.985642Z","submitted_at":"2026-07-14T18:15:32Z","title":"Text2Sign: A Single-GPU Diffusion Baseline for Text-to-Sign Language Video Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T06:05:02.778859Z"},"links":{"cited_paper":"/paper/2412.01199","citing_paper":"/paper/2607.13164"},"observation_digest":"sha256:4d1c880a183adfb62351f07a54ffc0c3a38c2b0980d46e7c099e2981d03cf923","observation_id":"e877f783-d8ed-4127-b657-6ba1c515c6ff","resolution":{"observed_at":"2026-08-02T06:05:02.778859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.01199/citation-record","integrity":"/paper/2412.01199/integrity","json":"/paper/2412.01199/citation-record.json","paper":"/paper/2412.01199"},"outbound":[],"paper":{"arxiv_id":"2412.01199","last_updated":"2024-12-02T07:05:39Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T17:07:55.964734Z","submitted_at":"2024-12-02T07:05:39Z","title":"TinyFusion: Diffusion Transformers Learned Shallow"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.01199."}