{"as_of":"2026-08-17T02:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8f57af6a5b044ba6d2281b8f6e78c9416188959484a56bf930966aa197213835","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:43:04.410440Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:09:21.954009Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T17:09:22.343165Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"cited_work":{"arxiv_id":"2504.20179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20179","snapshot_observed_at":"2026-08-15T17:09:22.343165Z","title":"Integration Flow Models","venue":"cs.CV","work_id":"14bd2094-9ca3-469d-ba51-4916730051f1","year":2025},"citing_paper":{"arxiv_id":"2508.17426","last_updated":"2025-08-24T16:00:08Z","snapshot_observed_at":"2026-08-16T13:52:42.412712Z","submitted_at":"2025-08-24T16:00:08Z","title":"Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T17:09:21.954009Z"},"links":{"cited_paper":"/paper/2504.20179","citing_paper":"/paper/2508.17426"},"observation_digest":"sha256:125b224769b2833a27cf9327caacdb66edc752d31ac5d0aea957289b82b817f9","observation_id":"d3a77e1f-9b7a-4eb8-96ef-18ac6392ddb3","resolution":{"observed_at":"2026-08-15T17:09:22.357817Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.20179/citation-record","integrity":"/paper/2504.20179/integrity","json":"/paper/2504.20179/citation-record.json","paper":"/paper/2504.20179"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15571","last_updated":"2023-03-09T15:18:40Z","snapshot_observed_at":"2026-08-14T22:23:53.150627Z","submitted_at":"2022-09-30T16:30:31Z","title":"Building Normalizing Flows with Stochastic Interpolants","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15571","snapshot_observed_at":"2026-08-16T05:43:04.288176Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.288176Z"},"links":{"cited_paper":"/paper/2209.15571","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:9fc89963a3a1f28c786802dffcca6c684938d652e6e8496ffdb9efaf9e77fe92","observation_id":"c63dba93-3bf2-4396-bff6-9603c325e6aa","resolution":{"observed_at":"2026-08-16T05:43:04.288176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06807","last_updated":"2024-11-19T14:31:02Z","snapshot_observed_at":"2026-08-16T14:10:56.813767Z","submitted_at":"2024-03-11T15:26:34Z","title":"Multistep Consistency Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06807","snapshot_observed_at":"2026-08-16T05:43:04.325071Z","title":"Multistep con- sistency models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.325071Z"},"links":{"cited_paper":"/paper/2403.06807","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:5a0d5c9899f6c6cf4b76f3de078d74b490ed1200cc55f24c9a8ccbd397fdacb6","observation_id":"9c70adc5-bdc7-4458-8e94-d5fb0d0e32ee","resolution":{"observed_at":"2026-08-16T05:43:04.325071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.17091","last_updated":"2023-06-04T22:19:27Z","snapshot_observed_at":"2026-08-16T20:12:05.932552Z","submitted_at":"2022-11-28T20:04:12Z","title":"Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.17091","snapshot_observed_at":"2026-08-16T05:43:04.335049Z","title":"J., Kang, W., and Moon, I.- C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.335049Z"},"links":{"cited_paper":"/paper/2211.17091","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:ecea892951600da265b426866547b66a0418286054e5d3c489affb9df564d470","observation_id":"fef5c92e-6965-4c1f-b103-8c16ef864d9e","resolution":{"observed_at":"2026-08-16T05:43:04.335049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.811262Z","title":"9 Submission for ICML 2025 Krizhevsky, A., Hinton, G., et al","venue":null,"work_id":"7e2a84db-4fa1-42be-9a41-3b5b66f4d002","year":2025},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.340113Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:a1293aa20b27ae57403236ea9470aab69dc12ede09294c08109708e851c67436","observation_id":"5ee157bc-422c-40fd-b0c8-6525fe32d800","resolution":{"observed_at":"2026-08-16T05:43:04.816150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-16T05:43:04.355279Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.355279Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:71b2c9f18d5b24a785e926d26fe0a470da235cb8928fbe66e20fd8bca717179c","observation_id":"f4179777-c48d-46ad-a331-6ddf69145506","resolution":{"observed_at":"2026-08-16T05:43:04.355279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.02388","last_updated":"2021-01-07T06:12:28Z","snapshot_observed_at":"2026-08-16T11:24:54.113126Z","submitted_at":"2021-01-07T06:12:28Z","title":"Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.02388","snapshot_observed_at":"2026-08-16T05:43:04.360109Z","title":"and Luhman, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.360109Z"},"links":{"cited_paper":"/paper/2101.02388","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:3e616abda4ee8c0bdd7e1d30233e5aa2c632ef0049a0b380201c07c1f846f136","observation_id":"7fed9762-1d58-4315-a9c7-4d0c315568f5","resolution":{"observed_at":"2026-08-16T05:43:04.360109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18455","last_updated":"2024-01-15T07:51:23Z","snapshot_observed_at":"2026-08-16T15:28:48.012680Z","submitted_at":"2023-05-29T04:22:57Z","title":"Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18455","snapshot_observed_at":"2026-08-16T05:43:04.365038Z","title":"Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.365038Z"},"links":{"cited_paper":"/paper/2305.18455","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:ddafe10e11f8307766e5e2d3e332c4330f425ea6a2d7398dcfba227d41806d81","observation_id":"ca0974d9-0d24-4c48-9243-fa6b71aa4ad1","resolution":{"observed_at":"2026-08-16T05:43:04.365038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.09515","last_updated":"2023-01-23T16:05:45Z","snapshot_observed_at":"2026-08-16T16:00:42.758116Z","submitted_at":"2023-01-23T16:05:45Z","title":"StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.09515","snapshot_observed_at":"2026-08-16T05:43:04.370040Z","title":"Song, J., Meng, C., and Ermon, S","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.370040Z"},"links":{"cited_paper":"/paper/2301.09515","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:b1799916c5459a104eb2fba7c7c2098b079c25f22fedde708e2f23b5dea1c8a9","observation_id":"7bbde87d-320a-40c1-8ab8-3d29845e1dd2","resolution":{"observed_at":"2026-08-16T05:43:04.370040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-16T05:43:04.375141Z","title":"P., Kumar, A., Er- mon, S., and Poole, B","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.375141Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:8487fdb2af1c301a81d723b3741f6981b7d25e1bc0fcd888406fce7f245555df","observation_id":"68b815d7-93c9-4a7b-925e-b390ab8a64cb","resolution":{"observed_at":"2026-08-16T05:43:04.375141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.379984Z","title":"Dropout: a simple way to prevent neural networks from overfitting","venue":null,"work_id":null,"year":1929},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.379984Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:3e186ed55888b1a813e4f2f4553293e3864879465c204141cbc220c9850cd28f","observation_id":"81a6b7d0-4763-4cb9-a1c9-d8c6e5bf3682","resolution":{"observed_at":"2026-08-16T05:43:04.379984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02398","last_updated":"2024-07-02T16:15:37Z","snapshot_observed_at":"2026-08-16T13:37:41.452512Z","submitted_at":"2024-07-02T16:15:37Z","title":"Consistency Flow Matching: Defining Straight Flows with Velocity Consistency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02398","snapshot_observed_at":"2026-08-16T05:43:04.385460Z","title":"Consistency flow matching: Defining straight flows with velocity consis- tency","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.385460Z"},"links":{"cited_paper":"/paper/2407.02398","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:9596219c8dc72cd72672945211f30f18477431241addca8f155479b1e12bc618","observation_id":"085d0c06-fd81-45e8-b58a-5cb462ffa997","resolution":{"observed_at":"2026-08-16T05:43:04.385460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13540","last_updated":"2024-05-31T05:15:40Z","snapshot_observed_at":"2026-08-16T13:50:40.476415Z","submitted_at":"2024-05-22T11:20:32Z","title":"Directly Denoising Diffusion Models","version":2},"cited_work":{"arxiv_id":"2405.13540","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.13540","snapshot_observed_at":"2026-08-16T05:43:04.491481Z","title":"Directly Denoising Diffusion Models","venue":"cs.CV","work_id":"1a039620-ec11-4466-a6f1-659c4dcbc37e","year":2024},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.390456Z"},"links":{"cited_paper":"/paper/2405.13540","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:7cbaffeaad98f03bd895b57118dcbb3b475e55b7392062cb29759ac087dd00f7","observation_id":"9b6e4b00-315e-44d6-a1e3-ef78f110dd08","resolution":{"observed_at":"2026-08-16T05:43:04.499344Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.13902","last_updated":"2023-02-25T20:30:35Z","snapshot_observed_at":"2026-08-16T17:03:46.549576Z","submitted_at":"2022-04-29T06:32:38Z","title":"Fast Sampling of Diffusion Models with Exponential Integrator","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.13902","snapshot_observed_at":"2026-08-16T05:43:04.395839Z","title":"and Chen, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.395839Z"},"links":{"cited_paper":"/paper/2204.13902","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:092a6edd40c38775ce54326e6afffbe575f2bea0b3bd75ca6eab9e67c4858e7b","observation_id":"d2acf03c-d793-419c-aea0-a9d6ea69a32e","resolution":{"observed_at":"2026-08-16T05:43:04.395839Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04867","last_updated":"2023-10-17T04:13:57Z","snapshot_observed_at":"2026-08-16T15:56:26.765986Z","submitted_at":"2023-02-09T18:59:48Z","title":"UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04867","snapshot_observed_at":"2026-08-16T05:43:04.400823Z","title":"Unipc: A unified predictor-corrector framework for fast sampling of diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.400823Z"},"links":{"cited_paper":"/paper/2302.04867","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:d6c5e92e32545acddad14ef0e4a7dbfe6177cb6069c22def574af79bc6177b2e","observation_id":"67541521-c775-4e24-a85a-218d5f562774","resolution":{"observed_at":"2026-08-16T05:43:04.400823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.785075Z","title":"Derivation of Integration Flow Algorithms A.1","venue":null,"work_id":"8df18c83-7b28-4ef9-8d03-c75508f86586","year":2025},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.405467Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:5cf574a075d089242e7b0bb0ed1395589468c03c62eeea1cbcf2fd829757ebe7","observation_id":"96b7767c-26bf-4178-a1ca-1ab903330f5d","resolution":{"observed_at":"2026-08-16T05:43:04.790150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.768304Z","title":null,"venue":null,"work_id":"4eec3084-6dfe-4c23-9adb-b80dec852b10","year":2025},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.410440Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:25b7b2d8237f9875e5a57e4768434a01060cd607be2d67e2cc8e0e740d399fcc","observation_id":"e731f158-3e17-4eb3-be44-1362baa87678","resolution":{"observed_at":"2026-08-16T05:43:04.773193Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.304492Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":1997,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.304492Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:e461b850e31bc0573db48ebbf17d8b830e08f29277f2e9d2caf56738be43a5a3","observation_id":"c6d1efab-753c-48f6-9e9e-9dd927ebc0f6","resolution":{"observed_at":"2026-08-16T05:43:04.304492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20320","last_updated":"2024-10-08T21:40:13Z","snapshot_observed_at":"2026-08-16T13:47:38.568076Z","submitted_at":"2024-05-30T17:56:04Z","title":"Improving the Training of Rectified Flows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20320","snapshot_observed_at":"2026-08-16T05:43:04.345333Z","title":"Improving the training of rectified flows","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.345333Z"},"links":{"cited_paper":"/paper/2405.20320","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:38d0249bf6146eba6c8224ed134dac52b9235056e94f3dffbb0a85aff55a5ece","observation_id":"e83e42d2-a6a5-476d-ab3f-7065b4bbb548","resolution":{"observed_at":"2026-08-16T05:43:04.345333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:04.309306Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.309306Z"},"links":{"citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:b392221f6b31c2e22042717d05d997fd45154a1b564225c9964e248b00d5f22f","observation_id":"eddba640-be49-4741-a275-fc951613b10c","resolution":{"observed_at":"2026-08-16T05:43:04.309306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05544","last_updated":"2023-06-08T20:30:55Z","snapshot_observed_at":"2026-08-16T15:25:20.735018Z","submitted_at":"2023-06-08T20:30:55Z","title":"BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05544","snapshot_observed_at":"2026-08-16T05:43:04.320173Z","title":"Boot: Data-free distillation of denoising diffusion models with bootstrapping","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.320173Z"},"links":{"cited_paper":"/paper/2306.05544","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:25fec0b32ecca59a2ca41b00cdd836132132b091b291e37a172b25188157474c","observation_id":"0b178d0e-a326-4a76-9ec7-7b8491d18fcf","resolution":{"observed_at":"2026-08-16T05:43:04.320173Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.11972","last_updated":"2023-06-14T03:32:57Z","snapshot_observed_at":"2026-08-16T16:06:32.038168Z","submitted_at":"2022-12-22T18:55:45Z","title":"Scalable Adaptive Computation for Iterative Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.11972","snapshot_observed_at":"2026-08-16T05:43:04.330605Z","title":"Scalable adaptive computation for iterative generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.330605Z"},"links":{"cited_paper":"/paper/2212.11972","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:05eee1354c3b152902c827225b7495112aa054b957abc9c524fb05e8f6c20cab","observation_id":"da350a72-bf2b-4f32-8c08-1573d99b8418","resolution":{"observed_at":"2026-08-16T05:43:04.330605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10298","last_updated":"2019-07-01T17:54:01Z","snapshot_observed_at":"2026-08-14T17:10:38.611122Z","submitted_at":"2019-02-27T01:48:32Z","title":"ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10298","snapshot_observed_at":"2026-08-16T05:43:04.314690Z","title":"Anode: Uncondi- tionally accurate memory-efficient gradients for neural odes","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.314690Z"},"links":{"cited_paper":"/paper/1902.10298","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:5e54fe808bad5379c375d62c41875f29aaa2c594f558300507beb18c521092c9","observation_id":"46435179-ea3b-4f9b-8dfe-d6a818733701","resolution":{"observed_at":"2026-08-16T05:43:04.314690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.04248","last_updated":"2023-03-07T21:46:15Z","snapshot_observed_at":"2026-08-16T15:49:59.625995Z","submitted_at":"2023-03-07T21:46:15Z","title":"TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.04248","snapshot_observed_at":"2026-08-16T05:43:04.293988Z","title":"A., Zhai, S., Hu, S., Zheng, D., Talbot, W., and Gu, E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.293988Z"},"links":{"cited_paper":"/paper/2303.04248","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:f539407a95bb5612ef573b366b6b054e177d297a2edeb40212a77396b765665a","observation_id":"8a22df99-79ab-4dc9-a7fe-a0d5a75d7846","resolution":{"observed_at":"2026-08-16T05:43:04.293988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.11096","last_updated":"2019-02-25T21:32:06Z","snapshot_observed_at":"2026-07-06T07:04:57.275371Z","submitted_at":"2018-09-28T15:38:49Z","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.11096","snapshot_observed_at":"2026-08-16T05:43:04.299186Z","title":"Large scale gan training for high fidelity natural image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.299186Z"},"links":{"cited_paper":"/paper/1809.11096","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:eed79887de12d8dd87d00584a31d3dee302a1accea2a52863826d15c9e2355da","observation_id":"7a4ecda2-31ac-472b-9921-28e20f110d7b","resolution":{"observed_at":"2026-08-16T05:43:04.299186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-16T02:30:42.660030Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-16T05:43:04.350316Z","title":"T., Ben-Hamu, H., Nickel, M., and Le, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T05:43:04.350316Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2504.20179"},"observation_digest":"sha256:57d923d92130be44f3d46eb96921a456ef20c2e2af61e707ea3cd7a74e00a2b0","observation_id":"2a6d31f7-300f-44c0-8a96-ed522b8a8216","resolution":{"observed_at":"2026-08-16T05:43:04.350316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.20179","last_updated":"2025-04-28T18:29:15Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T17:47:26.139637Z","submitted_at":"2025-04-28T18:29:15Z","title":"Integration Flow Models"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":25},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2504.20179."}