{"as_of":"2026-08-24T02:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a1d24bb653a32a1651c71e1279d4496c1bc011193a884cf0da6d44f785d0abd3","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":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":32,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T06:02:15.761082Z","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":"2026-07-04T16:59:58.123921Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2410.06940","last_updated":"2025-06-18T04:35:42Z","snapshot_observed_at":"2026-07-06T19:30:26.233621Z","submitted_at":"2024-10-09T14:34:53Z","title":"Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think","version":4},"reference_index":126,"source":"arxiv_source","source_observed_at":"2026-05-12T15:09:36.982610Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2410.06940"},"observation_digest":"sha256:43eca23bc64af83996a7afec9e979409a557650ea20b363d6be7860c395f7ff3","observation_id":"98d46857-a121-4f3d-8f0b-e54de2b896f6","resolution":{"observed_at":"2026-05-12T15:09:37.088833Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-12T13:22:08.483335Z","title":"De- constructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16308","last_updated":"2025-04-01T08:48:53Z","snapshot_observed_at":"2026-08-20T01:31:41.829558Z","submitted_at":"2024-11-25T11:53:55Z","title":"An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:22:08.483335Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2411.16308"},"observation_digest":"sha256:bd63ee16520efe54aee8b232f281d79377c10348e5f5ce4cdd2121e47a002d1d","observation_id":"6c8412a0-c0af-4c08-b3f2-c62d7b1e75d1","resolution":{"observed_at":"2026-08-12T13:22:08.483335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-11T20:10:47.398587Z","title":"Deconstructing denois- ing diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06016","last_updated":"2025-04-07T11:16:47Z","snapshot_observed_at":"2026-08-11T20:03:50.332267Z","submitted_at":"2024-12-08T18:21:00Z","title":"Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:47.398587Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2412.06016"},"observation_digest":"sha256:480267153d93ed3b1e3d3c3c508a9c3a549ececac7c1e08412d6f12122803168","observation_id":"4e76afc0-2536-4183-9529-8db7731041d7","resolution":{"observed_at":"2026-08-11T20:10:47.398587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-11T19:22:54.993646Z","title":"De- constructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06774","last_updated":"2024-12-09T18:57:24Z","snapshot_observed_at":"2026-08-19T18:18:47.660113Z","submitted_at":"2024-12-09T18:57:24Z","title":"Visual Lexicon: Rich Image Features in Language Space","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:22:54.993646Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2412.06774"},"observation_digest":"sha256:346026bc10296127eafe9898d4f47500e7f36f3abe53aefdc7327abc51cced2a","observation_id":"5ed146b8-028f-4577-a6e2-05d74d7da021","resolution":{"observed_at":"2026-08-11T19:22:54.993646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-11T17:42:11.604491Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.08781","last_updated":"2025-02-11T23:05:30Z","snapshot_observed_at":"2026-08-16T02:07:29.093190Z","submitted_at":"2024-12-11T21:23:24Z","title":"GMem: A Modular Approach for Ultra-Efficient Generative Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T17:42:11.604491Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2412.08781"},"observation_digest":"sha256:f27f6c2c68387ee0520da3aeeab8856c98fbcb254e9c8fa003a63522da462d30","observation_id":"fbc05112-b163-492b-8c5f-7727d957c114","resolution":{"observed_at":"2026-08-11T17:42:11.604491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-11T15:33:13.075744Z","title":"De- constructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10958","last_updated":"2025-03-14T22:22:40Z","snapshot_observed_at":"2026-08-19T12:26:32.736242Z","submitted_at":"2024-12-14T20:29:29Z","title":"SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:33:13.075744Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2412.10958"},"observation_digest":"sha256:60a6df60c7486f4637b5f09800e76f7ffab6e8c51aeb70cd12f330f00aa9abff","observation_id":"c8ca871d-15cf-45a1-bae3-ea3b314ddf6c","resolution":{"observed_at":"2026-08-11T15:33:13.075744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-11T01:02:55.977251Z","title":"De- constructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19104","last_updated":"2024-12-26T07:47:20Z","snapshot_observed_at":"2026-08-21T22:50:45.634933Z","submitted_at":"2024-12-26T07:47:20Z","title":"Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T01:02:55.977251Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2412.19104"},"observation_digest":"sha256:bfac3b9a2d0974f49d14426e5eaed5cd16543976b0371e29690938b7b263a30f","observation_id":"d7d51be8-6c58-424d-809a-3ea35b81b605","resolution":{"observed_at":"2026-08-11T01:02:55.977251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-08T15:06:47.179800Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06552","last_updated":"2025-02-10T15:20:07Z","snapshot_observed_at":"2026-08-20T09:02:33.005705Z","submitted_at":"2025-02-10T15:20:07Z","title":"Diffusion Models for Computational Neuroimaging: A Survey","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T15:06:47.179800Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2502.06552"},"observation_digest":"sha256:03e3fefd6600f0dd3194ea794e200e1b9ad545a04c9768882162a5372c08cf44","observation_id":"9431aabb-2c9d-40f4-ba51-2ffabfb72db8","resolution":{"observed_at":"2026-08-08T15:06:47.179800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-16T06:02:15.761082Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19353","last_updated":"2025-04-27T20:38:24Z","snapshot_observed_at":"2026-08-20T14:32:02.720323Z","submitted_at":"2025-04-27T20:38:24Z","title":"Flow Along the K-Amplitude for Generative Modeling","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T06:02:15.761082Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2504.19353"},"observation_digest":"sha256:a000b4fab62375d1481c0202afb44d94dfd245fed8247026e6c88a59c46866f1","observation_id":"38112896-a993-46a7-82e3-145467f5a68b","resolution":{"observed_at":"2026-08-16T06:02:15.761082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-16T05:18:15.341403Z","title":"De- constructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20996","last_updated":"2025-04-29T17:59:45Z","snapshot_observed_at":"2026-08-17T22:50:43.574612Z","submitted_at":"2025-04-29T17:59:45Z","title":"X-Fusion: Introducing New Modality to Frozen Large Language Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-16T05:18:15.341403Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2504.20996"},"observation_digest":"sha256:f2895a598b4d5014abada46db7523e9479170ae6881d63a6b155a1a90899f4ba","observation_id":"2bfbed52-4bd2-4001-a235-7cf0989566d6","resolution":{"observed_at":"2026-08-16T05:18:15.341403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-15T23:23:46.353650Z","title":"Deconstructing denoising diffusion models for self-supervised learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.04956","last_updated":"2025-05-08T05:38:19Z","snapshot_observed_at":"2026-08-20T23:08:49.209156Z","submitted_at":"2025-05-08T05:38:19Z","title":"Graffe: Graph Representation Learning via Diffusion Probabilistic Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T23:23:46.353650Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2505.04956"},"observation_digest":"sha256:4f8046057090ebad1035155b7d03a0e233c9a247f42e5f38b4521eb20f225299","observation_id":"123cce83-0199-4f95-8166-3bbf0feb0f2c","resolution":{"observed_at":"2026-08-15T23:23:46.353650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-15T23:02:08.495723Z","title":"Deconstructing denoising diffusion models for self- supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05732","last_updated":"2025-05-09T02:10:46Z","snapshot_observed_at":"2026-08-22T05:33:53.606328Z","submitted_at":"2025-05-09T02:10:46Z","title":"Automated Learning of Semantic Embedding Representations for Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:02:08.495723Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2505.05732"},"observation_digest":"sha256:b910786ce0d36b04ca5330f8797549deeada6500a625ec4d1fe890064f5dfff2","observation_id":"fcecddff-5f85-4900-a5eb-7b84b16608a9","resolution":{"observed_at":"2026-08-15T23:02:08.495723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-15T22:35:15.455562Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.06890","last_updated":"2025-05-11T08:03:18Z","snapshot_observed_at":"2026-08-19T18:21:24.703263Z","submitted_at":"2025-05-11T08:03:18Z","title":"Image Classification Using a Diffusion Model as a Pre-Training Model","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T22:35:15.455562Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2505.06890"},"observation_digest":"sha256:c080866e9cad7df39bddf643ee72653323fe6c3cd7243a79a3089c5cd1d1e165","observation_id":"49400acf-bc5c-4177-a4cf-59a63f5a8586","resolution":{"observed_at":"2026-08-15T22:35:15.455562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-07T14:16:32.356385Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19544","last_updated":"2025-05-26T06:05:29Z","snapshot_observed_at":"2026-08-09T12:20:57.513529Z","submitted_at":"2025-05-26T06:05:29Z","title":"Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:16:32.356385Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2505.19544"},"observation_digest":"sha256:a279b9e6e71ef572dcdc1852a091a5c77f0e24dd91df1c553c7aefddbcf2af15","observation_id":"3e47e6d0-928a-4940-830a-3f8c301c4d56","resolution":{"observed_at":"2026-08-07T14:16:32.356385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-07T04:43:05.854243Z","title":"Deconstructing denoising diffusion models for self-supervised learning.arXiv preprint arXiv:2401.14404, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09955","last_updated":"2025-06-11T17:28:52Z","snapshot_observed_at":"2026-08-22T03:24:15.816160Z","submitted_at":"2025-06-11T17:28:52Z","title":"Canonical Latent Representations in Conditional Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:43:05.854243Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2506.09955"},"observation_digest":"sha256:9d27e977285b6c5955e21e7be6940cbb2d0f821435a972c88893e054819e3d16","observation_id":"9effd71f-db1c-45e4-beac-2033421c43bb","resolution":{"observed_at":"2026-08-07T04:43:05.854243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-06T18:54:03.184468Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07104","last_updated":"2025-07-11T03:43:50Z","snapshot_observed_at":"2026-08-13T04:04:58.548265Z","submitted_at":"2025-07-09T17:59:04Z","title":"Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:54:03.184468Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2507.07104"},"observation_digest":"sha256:e8c883146f65782f35dfc51ad2fa993e5ca12f6ebc1ccec315706c458069a4e9","observation_id":"8edae33d-08d3-4730-bb8d-fc6fcbf02bf1","resolution":{"observed_at":"2026-08-06T18:54:03.184468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-06T04:37:44.402397Z","title":"In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 11621–11631","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.03249","last_updated":"2025-08-05T09:28:05Z","snapshot_observed_at":"2026-08-17T03:32:40.502441Z","submitted_at":"2025-08-05T09:28:05Z","title":"Visualising relativistic effects in redshift space distortions of large scale structure","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T04:37:44.402397Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2508.03249"},"observation_digest":"sha256:b7400d7c82bd45c18072deeab473f6f0b23151cfd3dbc9aef791573d627d584d","observation_id":"ec37f750-0f28-4666-9682-a4b763fc6545","resolution":{"observed_at":"2026-08-06T04:37:44.402397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-05T22:51:58.987307Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.06291","last_updated":"2025-08-08T13:11:54Z","snapshot_observed_at":"2026-08-20T12:50:59.514579Z","submitted_at":"2025-08-08T13:11:54Z","title":"Real-Time 3D Vision-Language Embedding Mapping","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:58.987307Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2508.06291"},"observation_digest":"sha256:9464c01273aa48a64c0af1a4f88e022d236d56fac4e08e6d6a767a534474cb85","observation_id":"095642e9-b2f5-47e8-8e79-ec96c9570365","resolution":{"observed_at":"2026-08-05T22:51:58.987307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-05T22:51:59.142839Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.06291","last_updated":"2025-08-08T13:11:54Z","snapshot_observed_at":"2026-08-20T12:50:59.514579Z","submitted_at":"2025-08-08T13:11:54Z","title":"Real-Time 3D Vision-Language Embedding Mapping","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:59.142839Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2508.06291"},"observation_digest":"sha256:3ea84072613e6a2ed16fe1ae4caca2278dd1b597f7882092a16e91aa4f989b0b","observation_id":"1a260cd4-33ea-47ce-8f77-50b4789b6c0a","resolution":{"observed_at":"2026-08-05T22:51:59.142839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-03T05:00:53.932417Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.03564","last_updated":"2026-07-11T18:04:42Z","snapshot_observed_at":"2026-08-14T14:13:51.565323Z","submitted_at":"2026-02-03T14:08:10Z","title":"CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T05:00:53.932417Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2602.03564"},"observation_digest":"sha256:adfe1ee5c0a09c1f752011a66146ff1bf2f50d2c344c4ab7978e2d74156bab73","observation_id":"dab488d4-2ac8-41d6-96f2-e9c691ae22d2","resolution":{"observed_at":"2026-08-03T05:00:53.932417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-02T22:25:58.514036Z","title":"Deconstructing denoising diffusion models for self-supervised learning.arXiv preprint arXiv:2401.14404,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.16918","last_updated":"2026-07-13T23:32:16Z","snapshot_observed_at":"2026-08-17T15:57:08.667030Z","submitted_at":"2026-02-18T22:22:44Z","title":"Xray-Visual Models: Scaling Vision models on Industry Scale Data","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T22:25:58.514036Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2602.16918"},"observation_digest":"sha256:88d8af6d9aaadf2478ac6809b6f67553b5618b4b7e4bbc93031f5244b35f1361","observation_id":"e0fc1917-471d-412f-96ca-d39244f146d2","resolution":{"observed_at":"2026-08-02T22:25:58.514036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-13T22:10:29.821610Z","title":"arXiv preprint arXiv:2401.14404 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.19122","last_updated":"2026-07-01T11:37:46Z","snapshot_observed_at":"2026-08-19T02:00:52.543791Z","submitted_at":"2026-03-19T16:44:23Z","title":"Revisiting Autoregressive Models for Generative Image Classification","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T22:10:29.821610Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2603.19122"},"observation_digest":"sha256:80910c5454a844efa6aac4aff5059ecbc33df43cd527c17489254f6636df653b","observation_id":"470c1ddd-b00b-4523-8dad-0cd1b0a2f1c1","resolution":{"observed_at":"2026-07-13T22:10:29.821610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2604.21502","last_updated":"2026-05-22T15:42:19Z","snapshot_observed_at":"2026-08-04T02:10:59.871292Z","submitted_at":"2026-04-23T10:04:36Z","title":"VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-09T21:38:15.877684Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2604.21502"},"observation_digest":"sha256:174d24ee9703450178cbc3e589a186a94041b6566c33ccec9f023fe39686b008","observation_id":"4962d101-5b78-45a2-8327-81d1b2cfd281","resolution":{"observed_at":"2026-05-11T14:31:07.303076Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2604.21502","last_updated":"2026-05-22T15:42:19Z","snapshot_observed_at":"2026-08-04T02:10:59.871292Z","submitted_at":"2026-04-23T10:04:36Z","title":"VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-25T06:03:56.040612Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2604.21502"},"observation_digest":"sha256:749e91b6f84d5e239045fae71e964d0e629495ddd5713dd550ca58264a10d5e9","observation_id":"454317d4-9211-40fd-baf8-4dcd9e413d00","resolution":{"observed_at":"2026-05-25T06:05:26.293122Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2605.10019","last_updated":"2026-05-11T05:44:18Z","snapshot_observed_at":"2026-08-16T08:48:37.559402Z","submitted_at":"2026-05-11T05:44:18Z","title":"The two clocks and the innovation window: When and how generative models learn rules","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-12T03:15:45.257213Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2605.10019"},"observation_digest":"sha256:e0758e2e53d621c82f7d6ab5aa85c994ddf79d8ba37ba9231ea5453723bea761","observation_id":"355ff940-1c40-4809-80dd-856333aeb1e2","resolution":{"observed_at":"2026-05-12T03:16:18.610563Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2605.18714","last_updated":"2026-06-25T13:57:35Z","snapshot_observed_at":"2026-08-15T07:00:36.095755Z","submitted_at":"2026-05-18T17:46:46Z","title":"Semantic Generative Tuning for Unified Multimodal Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-20T11:32:24.007847Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2605.18714"},"observation_digest":"sha256:34520c366bab95422c6e7afe66dbbae257af5a6b85977ce8549e812da6138b66","observation_id":"b6b0dbda-32ec-4eb1-a512-9aeefe016301","resolution":{"observed_at":"2026-05-20T11:33:14.178608Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2605.18714","last_updated":"2026-06-25T13:57:35Z","snapshot_observed_at":"2026-08-15T07:00:36.095755Z","submitted_at":"2026-05-18T17:46:46Z","title":"Semantic Generative Tuning for Unified Multimodal Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T18:31:10.578558Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2605.18714"},"observation_digest":"sha256:14d81a7f8a85a0768775f48192abac59d21e6bf039dc2e853e153427c6681ab0","observation_id":"1ef9940f-ad48-4702-9956-cea6d8752e70","resolution":{"observed_at":"2026-06-30T18:35:00.285093Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2606.00583","last_updated":"2026-05-30T07:21:40Z","snapshot_observed_at":"2026-08-06T21:04:24.758200Z","submitted_at":"2026-05-30T07:21:40Z","title":"Improving Visual Representation Alignment Generation with GRPO","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T19:24:09.597127Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2606.00583"},"observation_digest":"sha256:9f1532c00d76c456fb9d2a03dd8eaa5a6466359ad31de5fcc2b500fbd128af98","observation_id":"4c120460-cab3-4e0f-9f46-4672cec761d1","resolution":{"observed_at":"2026-06-28T19:32:35.110845Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","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":107,"source":"arxiv_source","source_observed_at":"2026-06-26T00:06:11.951205Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2606.24888"},"observation_digest":"sha256:24be1fc6ae5c96b0453de92803f497b13ae912da4aaf7e4a2fbb9f798a893729","observation_id":"e21c703e-ec96-4275-b6f3-f16c7b796960","resolution":{"observed_at":"2026-07-04T16:59:58.125550Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2401.14404","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-07-04T16:59:58.123921Z","title":"Deconstructing denoising diffusion models for self-supervised learning","venue":null,"work_id":"f0d3150f-39b1-430a-8b69-3ba6cfd2c719","year":2024},"citing_paper":{"arxiv_id":"2606.30147","last_updated":"2026-07-28T02:42:40Z","snapshot_observed_at":"2026-08-16T17:13:20.608750Z","submitted_at":"2026-06-29T11:23:11Z","title":"T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-30T06:40:55.325625Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2606.30147"},"observation_digest":"sha256:b3cddc76ce9d6a826aa6a648fdd912d3a8eb1a4f027d79dd162cee0f46fecccd","observation_id":"634428de-0cd4-4e99-9799-1da7c3535d58","resolution":{"observed_at":"2026-06-30T06:44:18.964821Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-02T09:36:33.905536Z","title":"arXiv preprint arXiv:2401.14404 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.30147","last_updated":"2026-07-28T02:42:40Z","snapshot_observed_at":"2026-08-16T17:13:20.608750Z","submitted_at":"2026-06-29T11:23:11Z","title":"T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-02T09:36:33.905536Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2606.30147"},"observation_digest":"sha256:fa34692345290b4a2a0248b797b65eb921f03bb55cab9524389a2b8bb7f7c491","observation_id":"663d5918-ae0d-43fc-b0ce-7082bd31f7d4","resolution":{"observed_at":"2026-08-02T09:36:33.905536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14404","snapshot_observed_at":"2026-08-02T05:05:21.705341Z","title":"Deconstructing denoising diffusion models for self-supervised learning.arXiv preprint arXiv:2401.14404, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.13503","last_updated":"2026-07-15T06:55:52Z","snapshot_observed_at":"2026-08-17T11:04:50.794294Z","submitted_at":"2026-07-15T06:55:52Z","title":"Exploring the Alignment of Generation and Understanding in Protein Structure Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T05:05:21.705341Z"},"links":{"cited_paper":"/paper/2401.14404","citing_paper":"/paper/2607.13503"},"observation_digest":"sha256:de284017a8dbc939eaa22a3168f89e46425bcee853d61cf05ef96966539cdfa6","observation_id":"127abfb3-86cc-499b-9d58-17f2fdc4d9e0","resolution":{"observed_at":"2026-08-02T05:05:21.705341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.14404/citation-record","integrity":"/paper/2401.14404/integrity","json":"/paper/2401.14404/citation-record.json","paper":"/paper/2401.14404"},"outbound":[],"paper":{"arxiv_id":"2401.14404","last_updated":"2024-01-25T18:59:57Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-21T01:29:25.430869Z","submitted_at":"2024-01-25T18:59:57Z","title":"Deconstructing Denoising Diffusion Models for Self-Supervised Learning"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2401.14404."}