{"as_of":"2026-08-09T09:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8201a98185f9b5b6cbcbb0d0d9e64622ecffb92d959bcaa2047d59b28ddd8f39","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:37:42.076990Z","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":"2026-05-15T04:49:44.350666Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-08T22:37:42.076990Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transformers up, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04507","last_updated":"2025-06-04T23:21:39Z","snapshot_observed_at":"2026-08-08T22:25:30.997284Z","submitted_at":"2025-02-06T21:17:09Z","title":"Fast Video Generation with Sliding Tile Attention","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T22:37:42.076990Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2502.04507"},"observation_digest":"sha256:92e5bd5d0466252d7ef5208e64fc49b3df4b577ed412a56f2106df430271f8e5","observation_id":"ee90d107-e3d7-4b05-a824-932df35c9935","resolution":{"observed_at":"2026-08-08T22:37:42.076990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-07T15:33:10.540296Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transformers up","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14687","last_updated":"2025-05-20T17:59:59Z","snapshot_observed_at":"2026-08-08T00:07:41.253312Z","submitted_at":"2025-05-20T17:59:59Z","title":"Grouping First, Attending Smartly: Training-Free Acceleration for Diffusion Transformers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:33:10.540296Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2505.14687"},"observation_digest":"sha256:a274088f5343c1d06882164a26abb304aa17486f27415e1921f0cdbab8a15b5e","observation_id":"35b85fd9-8839-4faa-a332-90f6e4ea280d","resolution":{"observed_at":"2026-08-07T15:33:10.540296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-07T14:03:17.722525Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transform- ers up","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20171","last_updated":"2025-05-26T16:12:41Z","snapshot_observed_at":"2026-08-07T13:55:36.207486Z","submitted_at":"2025-05-26T16:12:41Z","title":"Long-Context State-Space Video World Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:03:17.722525Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2505.20171"},"observation_digest":"sha256:1831a8d37b753e1c2c0b501df637048065e6412684dc8faf2359c4615817a6b1","observation_id":"78d5b7f4-987c-41d8-8fff-0866a7dab333","resolution":{"observed_at":"2026-08-07T14:03:17.722525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-07T11:15:16.170658Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transformers up.arXiv preprint arXiv:2412.16112, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03065","last_updated":"2025-06-03T16:42:37Z","snapshot_observed_at":"2026-08-08T23:56:38.842535Z","submitted_at":"2025-06-03T16:42:37Z","title":"Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:16.170658Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2506.03065"},"observation_digest":"sha256:8f2f180675fcb45e7331ab7b558deb262f416b5d7b9a1aee42bd7a4df8b6e53e","observation_id":"819697d8-6b6f-453f-afef-8326d1911db9","resolution":{"observed_at":"2026-08-07T11:15:16.170658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-07T10:29:04.559699Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transformers up","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":46,"source":"pdf_text","source_observed_at":"2026-08-07T10:29:04.559699Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2506.05340"},"observation_digest":"sha256:483dba54721953866da893ac1ea6cfe33f8f29766c605af19bd88d61b9ae7bfa","observation_id":"08c57e6b-7b12-4d86-878d-6a1237331c3a","resolution":{"observed_at":"2026-08-07T10:29:04.559699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-06T15:22:26.319986Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Trans- formers Up","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16154","last_updated":"2025-07-22T02:05:21Z","snapshot_observed_at":"2026-08-07T01:34:53.451214Z","submitted_at":"2025-07-22T02:05:21Z","title":"LSSGen: Leveraging Latent Space Scaling in Flow and Diffusion for Efficient Text to Image Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:22:26.319986Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2507.16154"},"observation_digest":"sha256:6e8dd58d7b10454618ff006f2c54172bdd3e1c30c376496b5715c058962c827b","observation_id":"f43143fe-743a-4133-9c89-30880657aac2","resolution":{"observed_at":"2026-08-06T15:22:26.319986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-04T09:40:30.003576Z","title":"Clear: Conv-like lineariza- tion revs pre-trained diffusion transformers up,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.14260","last_updated":"2026-06-29T08:31:48Z","snapshot_observed_at":"2026-08-05T04:17:49.967442Z","submitted_at":"2025-10-16T03:21:28Z","title":"MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matching","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T09:40:30.003576Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2510.14260"},"observation_digest":"sha256:50cf1b615aa93ce43c5eb09e866e8dc7082e5b211f61a47ebac168518e901770","observation_id":"4c234ab7-1ce2-4bd1-afbf-988e6a20e5ad","resolution":{"observed_at":"2026-08-04T09:40:30.003576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-08-03T20:52:38.436119Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transform- ers up.arXiv preprint arXiv:2412.16112, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.18050","last_updated":"2025-11-22T13:07:21Z","snapshot_observed_at":"2026-08-06T13:47:39.734426Z","submitted_at":"2025-11-22T13:07:21Z","title":"UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T20:52:38.436119Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2511.18050"},"observation_digest":"sha256:9877675a547ea1a76df4ca32b08e66d8fde132fb4a48b3c1cfebe05005919450","observation_id":"cc50726c-6815-4b30-bf52-a28e30d53595","resolution":{"observed_at":"2026-08-03T20:52:38.436119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":"2412.16112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transform- ers up","venue":null,"work_id":"7ee65050-cb76-4277-a0b9-7667bddef407","year":2024},"citing_paper":{"arxiv_id":"2605.02772","last_updated":"2026-05-28T08:32:22Z","snapshot_observed_at":"2026-08-03T12:45:11.934276Z","submitted_at":"2026-05-04T16:16:26Z","title":"Linearizing Vision Transformer with Test-Time Training","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-08T18:25:48.672665Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2605.02772"},"observation_digest":"sha256:8f59c7c4bc2cfa4d09e897381b0185d6f8ec394cc2e81be567cd5d97979acbcb","observation_id":"0edbb81d-4ec5-49fd-9a5a-f042d4706505","resolution":{"observed_at":"2026-05-09T06:25:48.635059Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2412.16112","last_updated":"2024-12-20T17:57:09Z","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up","version":1},"cited_work":{"arxiv_id":"2412.16112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.16112","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Clear: Conv-like linearization revs pre-trained diffusion transform- ers up","venue":null,"work_id":"7ee65050-cb76-4277-a0b9-7667bddef407","year":2024},"citing_paper":{"arxiv_id":"2605.14191","last_updated":"2026-05-13T23:13:29Z","snapshot_observed_at":"2026-08-01T23:13:44.447855Z","submitted_at":"2026-05-13T23:13:29Z","title":"CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T04:47:32.476614Z"},"links":{"cited_paper":"/paper/2412.16112","citing_paper":"/paper/2605.14191"},"observation_digest":"sha256:85f33d7b99d75aaedb08dce114ab670843d87083e41509c032430c3557532695","observation_id":"720c137d-6534-4634-bf8d-eea77eba189e","resolution":{"observed_at":"2026-05-15T04:49:44.353981Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2412.16112/citation-record","integrity":"/paper/2412.16112/integrity","json":"/paper/2412.16112/citation-record.json","paper":"/paper/2412.16112"},"outbound":[],"paper":{"arxiv_id":"2412.16112","last_updated":"2024-12-20T17:57:09Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T20:11:06.248415Z","submitted_at":"2024-12-20T17:57:09Z","title":"CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up"},"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 10 inbound Pith citation observations for arXiv:2412.16112."}