{"as_of":"2026-08-16T07:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:025a3b3eb35d23fc823b60488ef7ca3f0b053269a9f5308a38b506304a23c699","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:04:29.619116Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"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":"2026-08-04T19:11:59.590780Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.09464","snapshot_observed_at":"2026-08-04T19:11:59.590780Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.09748","last_updated":"2025-09-11T12:32:08Z","snapshot_observed_at":"2026-08-08T07:23:28.359307Z","submitted_at":"2025-09-11T12:32:08Z","title":"DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T19:11:59.590780Z"},"links":{"cited_paper":"/paper/2501.09464","citing_paper":"/paper/2509.09748"},"observation_digest":"sha256:8f316824e6ca6e7c7ca3451974cf02fd26d5423890ca257a1ae7f1603aa6e314","observation_id":"e2cbe16c-8873-48a8-aad6-302e03ab9c91","resolution":{"observed_at":"2026-08-04T19:11:59.590780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2501.09464/citation-record","integrity":"/paper/2501.09464/integrity","json":"/paper/2501.09464/citation-record.json","paper":"/paper/2501.09464"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:04:30.265520Z","title":"”Denoising Diffusion Prob- abilistic Models.” Advances in Neural Information Processing Systems (NeurIPS), 2020","venue":null,"work_id":"dfea0479-6d91-4dfa-b764-57e968261a61","year":2020},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.452758Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:15359e52dad8bb3525eea13bd1d621682d55565d7c3e71e911cb0b3f5b6ebe42","observation_id":"36e8a790-3758-466f-8a07-8f831271335b","resolution":{"observed_at":"2026-08-10T20:04:30.270586Z","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":"2102.09672","last_updated":"2021-02-18T23:44:17Z","snapshot_observed_at":"2026-08-16T00:48:59.926421Z","submitted_at":"2021-02-18T23:44:17Z","title":"Improved Denoising Diffusion Probabilistic Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.09672","snapshot_observed_at":"2026-08-10T20:04:29.457700Z","title":"”Improved Denoising Diffusion Probabilistic Models.” arXiv preprint arXiv:2102.09672, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.457700Z"},"links":{"cited_paper":"/paper/2102.09672","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:c8ca2362cd01e284756489debef714a7aa83ea8c3e9a86360fa2bdd24d199e17","observation_id":"a67cfef4-f267-4cec-a49c-2f6ae6b2da84","resolution":{"observed_at":"2026-08-10T20:04:29.457700Z","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":"2026-08-10T20:04:30.249454Z","title":"Kingma, Abhishek Ku- mar, Stefano Ermon, and Ben Poole","venue":null,"work_id":"c1319102-8223-4efb-963e-628d52535a6e","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.463272Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:a37072bd1b30a2013dd8f2701c26c360e188d2c0b36c32553850d15485792c6c","observation_id":"46e8fb3a-7e9c-4cf5-9a71-1a5008559ff5","resolution":{"observed_at":"2026-08-10T20:04:30.254947Z","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":"2107.00047","last_updated":"2021-09-30T02:19:26Z","snapshot_observed_at":"2026-08-13T18:54:02.515057Z","submitted_at":"2021-06-30T18:09:54Z","title":"Higher angular momentum pairings in interorbital shadowed-triplet superconductors: Application to Sr$_{2}$RuO$_{4}$","version":2},"cited_work":{"arxiv_id":"2107.00047","doi":null,"metadata_source":"pith","pith_arxiv_id":"2107.00047","snapshot_observed_at":"2026-08-10T20:04:29.965269Z","title":"Higher angular momentum pairings in interorbital shadowed-triplet superconductors: Application to Sr$_{2}$RuO$_{4}$","venue":"cond-mat.supr-con","work_id":"9f0dc0ac-4cfe-43c2-b668-69d3c2597ec5","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.468554Z"},"links":{"cited_paper":"/paper/2107.00047","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:0ad0288b7494f1d68715a3702d7d32f32a37334fe36735e1f42786537d3a1f59","observation_id":"73e28709-0531-4666-97d1-8931a10e3d32","resolution":{"observed_at":"2026-08-10T20:04:29.971209Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2106.02265","last_updated":"2021-06-04T05:14:51Z","snapshot_observed_at":"2026-08-11T00:47:36.562880Z","submitted_at":"2021-06-04T05:14:51Z","title":"The structure of the unit group of the group algebra $F(C_3 \\times D_{10})$","version":1},"cited_work":{"arxiv_id":"2106.02265","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.02265","snapshot_observed_at":"2026-08-10T20:04:29.941626Z","title":"The structure of the unit group of the group algebra $F(C_3 \\times D_{10})$","venue":"math.RA","work_id":"c56f35b2-b2b5-4ede-b8e5-75476d39d96e","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.473829Z"},"links":{"cited_paper":"/paper/2106.02265","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:3db60554fe0eb44ad3eaad39f15e95d23c16a40c54f3c39bc6f2ec7558e4c5ce","observation_id":"40588ea9-6bf1-4f3c-bfe0-bd459ca72cd9","resolution":{"observed_at":"2026-08-10T20:04:29.946822Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:04:30.232865Z","title":"Weiss, Mohammad Norouzi, and William Chan","venue":null,"work_id":"0148e647-8402-46a4-aeb5-f3fe759dbc47","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.479344Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:7ec95ec7c6513a5c92d8cc6e3cc08f014c466e6f363582525e7054c2c2edfdd1","observation_id":"32454064-77e6-47a7-94fa-905c8d00119d","resolution":{"observed_at":"2026-08-10T20:04:30.238033Z","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":"2026-08-10T20:04:30.216243Z","title":"”DiffWave: A Versatile Diffusion Model for Audio Synthesis.” International Conference on Learning Representations (ICLR), 2021","venue":null,"work_id":"534f8005-a44b-40a8-8550-6987d182aaeb","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.485783Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:bdf6e09a9ab549037ac2207021ce0aadc8718a277bf4db29fdce91b573ba2bf9","observation_id":"d4fc3c28-2b3d-4965-811f-41b136dbd79d","resolution":{"observed_at":"2026-08-10T20:04:30.221364Z","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":"2205.14217","last_updated":"2022-05-27T20:12:09Z","snapshot_observed_at":"2026-08-14T18:17:30.019563Z","submitted_at":"2022-05-27T20:12:09Z","title":"Diffusion-LM Improves Controllable Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.14217","snapshot_observed_at":"2026-08-10T20:04:29.490949Z","title":"”Diffusion-LM Improves Controllable Text Generation.” arXiv preprint arXiv:2205.14217 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.490949Z"},"links":{"cited_paper":"/paper/2205.14217","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:711d5867df4f09679e5ac5e8f48b8c67e1ac480455b1b511216c6aab61503e43","observation_id":"c79dd648-c3ae-4623-be5c-fb63609e4052","resolution":{"observed_at":"2026-08-10T20:04:29.490949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04023","last_updated":"2023-11-28T09:01:12Z","snapshot_observed_at":"2026-08-07T14:17:12.140094Z","submitted_at":"2023-02-08T12:35:34Z","title":"A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04023","snapshot_observed_at":"2026-08-10T20:04:29.496036Z","title":"”DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models.” arXiv preprint arXiv:2302.04023 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.496036Z"},"links":{"cited_paper":"/paper/2302.04023","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:42d1d283a381dec1e204095bb49a498d4e82e8e0dbc601dfa7ca50e98fb0c8fd","observation_id":"3cbeded0-87f7-4d86-9c61-a473b7cf8a58","resolution":{"observed_at":"2026-08-10T20:04:29.496036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.05233","last_updated":"2021-06-01T17:49:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-05-11T17:50:24Z","title":"Diffusion Models Beat GANs on Image Synthesis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.05233","snapshot_observed_at":"2026-08-10T20:04:29.501547Z","title":"”Elucidating the Design Space of Diffusion-Based Generative Models.” arXiv preprint arXiv:2105.05233 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.501547Z"},"links":{"cited_paper":"/paper/2105.05233","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:fba34d899887d92f5416584962dc55d4fa32eb0a60909c9ca524193095629f6e","observation_id":"974c78b6-2983-4e5f-b318-68a54e85cd5b","resolution":{"observed_at":"2026-08-10T20:04:29.501547Z","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":"2026-08-10T20:04:30.200709Z","title":"”Fast Sampling of Diffusion Models with Exponential Integrator.” Advances in Neural Information Processing Systems (NeurIPS), 2021","venue":null,"work_id":"8daed22a-fcbc-4e90-91bc-15bd7760edb9","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.506274Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:2ff699d9e01ec5eee1526387ab9648e46d249ce03031c0703121bb2d658fe8de","observation_id":"1da17136-a7b9-407d-aaf6-a4f51a87b154","resolution":{"observed_at":"2026-08-10T20:04:30.205955Z","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":"2204.07166","last_updated":"2022-04-14T07:07:41Z","snapshot_observed_at":"2026-08-13T16:02:33.713667Z","submitted_at":"2022-04-14T07:07:41Z","title":"Simulated assessment of light transport through ischaemic skin flaps","version":1},"cited_work":{"arxiv_id":"2204.07166","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.07166","snapshot_observed_at":"2026-08-10T20:04:29.862794Z","title":"Simulated assessment of light transport through ischaemic skin flaps","venue":"physics.med-ph","work_id":"21ae2e6c-1ab8-48f0-89eb-ba719cb642a4","year":2022},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.511358Z"},"links":{"cited_paper":"/paper/2204.07166","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:e2ec269dcf2616b77ffac1eac9b7b69a7149092551037b9a039ab95d8a2331e2","observation_id":"4aed746c-46e5-49ff-8c14-cc2b403661eb","resolution":{"observed_at":"2026-08-10T20:04:29.868440Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:04:30.184800Z","title":"”Kdgan: Knowledge distillation with generative ad- versarial networks.” Advances in neural information processing systems 31 (2018)","venue":null,"work_id":"48775195-bd60-4dbd-b93e-29291f2131e3","year":2018},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.516379Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:2a43a45089095bfd2f0e1e5ab5df67e1351dc04e2d76c280905e12d90fa703db","observation_id":"46180976-4d6a-4838-906e-69ac0023ea52","resolution":{"observed_at":"2026-08-10T20:04:30.189556Z","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":"2301.11309","last_updated":"2023-06-22T06:56:24Z","snapshot_observed_at":"2026-08-15T14:30:18.952664Z","submitted_at":"2023-01-26T18:49:02Z","title":"SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification","version":2},"cited_work":{"arxiv_id":"2301.11309","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.11309","snapshot_observed_at":"2026-08-10T20:04:29.840590Z","title":"SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification","venue":"cs.CL","work_id":"3ebb5492-ad67-40af-8883-3e16f04d0285","year":2023},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.520999Z"},"links":{"cited_paper":"/paper/2301.11309","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:15d4855a79589036ca30f3fb0ef4698bc9c0e41a10338789d2aa31a14562cfac","observation_id":"525765ae-2c98-4282-9b66-ac274a555619","resolution":{"observed_at":"2026-08-10T20:04:29.845994Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-10T20:04:29.525759Z","title":"”Denoising diffusion implicit models.” arXiv preprint arXiv:2010.02502 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.525759Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:0cb61c53cee82ce300d177e1476a9d000f7fd2aeb825b932861849b2bfdeb7c4","observation_id":"aa8410c7-640f-4033-8c94-3a91967bbe69","resolution":{"observed_at":"2026-08-10T20:04:29.525759Z","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":"2026-08-10T20:04:30.170257Z","title":"”Structural Pruning for Diffusion Models.” Advances in Neural Information Processing Systems (NeurIPS), 2023","venue":null,"work_id":"22184151-47e5-449b-a052-4a1037c1a5a4","year":2023},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.530541Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:9ded7c86a12233baea948b9f8742b0409882f114b10345827d1ab04f81857956","observation_id":"4cab3ba3-4e76-4581-97f8-51265906be3d","resolution":{"observed_at":"2026-08-10T20:04:30.175240Z","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":"2404.10445","last_updated":"2025-04-17T16:05:20Z","snapshot_observed_at":"2026-08-13T18:23:40.384759Z","submitted_at":"2024-04-16T10:31:06Z","title":"SparseDM: Toward Sparse Efficient Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10445","snapshot_observed_at":"2026-08-10T20:04:29.535162Z","title":"”SparseDM: Toward Sparse Efficient Diffusion Models.” arXiv preprint arXiv:2404.10445 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.535162Z"},"links":{"cited_paper":"/paper/2404.10445","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:fcedfe8c6ef4007427acdcacc56c800955c1422e25fc0dae798db2fbd06d2db8","observation_id":"c79327e5-685e-4985-9dd4-072aa6d5f5eb","resolution":{"observed_at":"2026-08-10T20:04:29.535162Z","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":"2026-08-10T20:04:30.154700Z","title":"Optimal transport: old and new","venue":null,"work_id":"483929fe-c0ba-426d-beff-3edcf6d60c81","year":2009},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.540045Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:ac5d4ad5416eb809f16b049d8082ab75dfdbdbb2732786117351acda939d0852","observation_id":"ee3f3b9e-5188-41ce-8a90-d973fa3088f1","resolution":{"observed_at":"2026-08-10T20:04:30.159846Z","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":"2002.07376","last_updated":"2020-08-07T00:02:33Z","snapshot_observed_at":"2026-08-10T04:53:00.900945Z","submitted_at":"2020-02-18T05:14:47Z","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07376","snapshot_observed_at":"2026-08-10T20:04:29.544583Z","title":"”Picking winning tickets before training by preserving gradient flow.” arXiv preprint arXiv:2002.07376 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.544583Z"},"links":{"cited_paper":"/paper/2002.07376","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:ed081161436855b6668fee3d35d42d537cd44415c2862714bd7a865eab7d98d1","observation_id":"64b813fb-67eb-4667-bd4d-bb8338ee31a5","resolution":{"observed_at":"2026-08-10T20:04:29.544583Z","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":"2026-08-10T20:04:30.138492Z","title":"”Soft masking for cost-constrained channel prun- ing.” European Conference on Computer Vision","venue":null,"work_id":"d4317aa3-a148-45fc-8cb5-07c5ca8ebe0e","year":2022},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.549729Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:0aac67a82d4432639f928332d5c36bdd7db31c5924d27d36dc6f240251d124bf","observation_id":"da6a49e0-51c2-4da0-b351-7fca9d507fcb","resolution":{"observed_at":"2026-08-10T20:04:30.144145Z","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":"2209.07617","last_updated":"2022-09-15T21:30:55Z","snapshot_observed_at":"2026-08-13T14:25:28.467421Z","submitted_at":"2022-09-15T21:30:55Z","title":"Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.07617","snapshot_observed_at":"2026-08-10T20:04:29.554318Z","title":"”Training recipe for n: M structured sparsity with decaying pruning mask.” arXiv preprint arXiv:2209.07617 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.554318Z"},"links":{"cited_paper":"/paper/2209.07617","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:6a286fbb999170a50be29e354e1f48b7f042392bc1da13467d5bf00b2fd9f6f4","observation_id":"de055d5a-2ba9-4c9a-870a-e49c214a8032","resolution":{"observed_at":"2026-08-10T20:04:29.554318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04010","last_updated":"2021-04-18T10:18:00Z","snapshot_observed_at":"2026-08-14T18:17:15.787177Z","submitted_at":"2021-02-08T05:55:47Z","title":"Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04010","snapshot_observed_at":"2026-08-10T20:04:29.559032Z","title":"”Learning n: m fine-grained structured sparse neural networks from scratch.” arXiv preprint arXiv:2102.04010 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.559032Z"},"links":{"cited_paper":"/paper/2102.04010","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:e1a928bac9bc901caf2aff6b389c7c5d38e68a416c118adc5a2d4ed3f0405dc7","observation_id":"14c752e4-ec20-4568-845a-31b473c18f58","resolution":{"observed_at":"2026-08-10T20:04:29.559032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.11839","last_updated":"2021-09-23T07:47:56Z","snapshot_observed_at":"2026-08-13T21:30:55.037811Z","submitted_at":"2020-09-24T17:37:32Z","title":"A Gradient Flow Framework For Analyzing Network Pruning","version":4},"cited_work":{"arxiv_id":"2009.11839","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.11839","snapshot_observed_at":"2026-08-10T20:04:29.732941Z","title":"A Gradient Flow Framework For Analyzing Network Pruning","venue":"cs.LG","work_id":"0ec77924-c7a8-434c-a0d1-927ba12bc190","year":2020},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.563678Z"},"links":{"cited_paper":"/paper/2009.11839","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:0e2b0eeb6303b536dd9e0077b37ef680a72c4523e6f955ddb5936e81cc1d581f","observation_id":"40ddcdab-37fe-4c51-a869-c7f4b44989a4","resolution":{"observed_at":"2026-08-10T20:04:29.740579Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:04:30.121508Z","title":"T., Wan, B., Zhang, H., Chen, J.,","venue":null,"work_id":"6fc3c046-af15-447f-9b9b-45915148cb42","year":2024},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.568032Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:06451bc471ee21f931272b6021722f6fc93538bebd82fe28fdf0e80e498d6a3a","observation_id":"b9cb37fb-701c-4122-8716-607945bcdc6a","resolution":{"observed_at":"2026-08-10T20:04:30.127153Z","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":"2026-08-10T20:04:30.105299Z","title":"T., Wan, B., Zhang, H., Chen, J., Wang, J., & Li, B","venue":null,"work_id":"cd18a771-9819-40ce-bd37-58bc9d345620","year":2025},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.572247Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:12a255fb9b0e15df1429096d822d2e9f4a40d6b16a633f626d8de50e5354a596","observation_id":"f2e0a7e7-eed0-4189-8c98-e4d05da81032","resolution":{"observed_at":"2026-08-10T20:04:30.110650Z","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":"2026-08-10T20:04:30.087788Z","title":null,"venue":null,"work_id":"acc00a56-92aa-4eda-83df-fb42b6a0495b","year":2024},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.576314Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:f07400c4fcaacac5ac7d258a9d750ded8d0cfa360b85455ef5ef5d78a937809e","observation_id":"97b03de5-bdc1-4006-a76a-ceeb4850bf22","resolution":{"observed_at":"2026-08-10T20:04:30.093930Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:04:30.071363Z","title":"”Only train once: A one-shot neural network training and pruning framework.” Advances in Neural Information Processing Systems 34 (2021): 19637-19651","venue":null,"work_id":"a9ca324e-2823-40fe-8e1c-9d91a933384e","year":2021},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.580350Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:8ebdd929dc97ddd9bb095eb1e64e9988f857fa07e48c535258ee6dbf5c5595ae","observation_id":"51a85230-abc3-409b-b32b-917796b5d198","resolution":{"observed_at":"2026-08-10T20:04:30.075783Z","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":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-14T19:37:43.556604Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-10T20:04:29.584828Z","title":"”The lottery ticket hy- pothesis: Finding sparse, trainable neural networks.” arXiv preprint arXiv:1803.03635 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.584828Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:57c767cda717c18257f00baa7d8192fb921514808b1b3dbd67a7d1f5bdaffb3b","observation_id":"9525d510-523a-43ed-9bdf-40913eb08ae2","resolution":{"observed_at":"2026-08-10T20:04:29.584828Z","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":"2026-08-10T20:04:30.056624Z","title":"”Learning multiple layers of features from tiny images.” (2009): 7","venue":null,"work_id":"619b6555-d3b4-415a-99db-89297bed860b","year":2009},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.589162Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:5965038218d4e948dd5180cc328a1dbb654e94ef128dadcb2be695e3cb99702f","observation_id":"3a6dc40b-7005-4caa-a337-14745c777bcb","resolution":{"observed_at":"2026-08-10T20:04:30.061268Z","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":"1710.10196","last_updated":"2018-02-26T15:33:34Z","snapshot_observed_at":"2026-08-15T04:29:56.166616Z","submitted_at":"2017-10-27T15:28:35Z","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10196","snapshot_observed_at":"2026-08-10T20:04:29.593405Z","title":"”Progressive Growing of GANs for Improved Quality, Stability, and Variation.” arXiv preprint arXiv:1710.10196 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.593405Z"},"links":{"cited_paper":"/paper/1710.10196","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:e601bd4d087f1ff75307aa36bbea954b3ab64b459e84b12462505bbb2de3cc26","observation_id":"186716bc-f45e-4414-a516-2137dc070a41","resolution":{"observed_at":"2026-08-10T20:04:29.593405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.03365","last_updated":"2016-06-04T09:51:30Z","snapshot_observed_at":"2026-08-08T13:26:17.517503Z","submitted_at":"2015-06-10T15:38:47Z","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.03365","snapshot_observed_at":"2026-08-10T20:04:29.598226Z","title":"”Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop.” arXiv preprint arXiv:1506.03365 (2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.598226Z"},"links":{"cited_paper":"/paper/1506.03365","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:03090d008c712b7641827e402a86a01a866a53f7e19d9ca7745d61b32215618c","observation_id":"6b66810d-062f-449a-953a-e7e4337a3cc4","resolution":{"observed_at":"2026-08-10T20:04:29.598226Z","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":"2026-08-10T20:04:30.038677Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","venue":null,"work_id":"0a7f37c9-6f8a-4d9e-be5d-b28f2ca47c4e","year":2017},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.604476Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:6282640ae84ae35ab5c09cf9ca6d41a0a15b2a3108469d5a8fe0b26afc41a4ff","observation_id":"54374657-5357-4301-b19d-b8afcbb3b9c3","resolution":{"observed_at":"2026-08-10T20:04:30.044570Z","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":"2026-08-10T20:04:30.022219Z","title":"Bovik, Hamid R","venue":null,"work_id":"f6224072-4ad1-4c2a-88f1-32c87d109925","year":2004},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.609313Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:790633c2459be58c29b1f66cf93da6ffa8ec3fa17f75926d146538044f10b6fe","observation_id":"8ad5731a-40fe-4c5d-a847-740d20171a94","resolution":{"observed_at":"2026-08-10T20:04:30.028497Z","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":"2026-08-10T20:04:29.999141Z","title":"Channel pruning for accelerat- ing very deep neural networks","venue":null,"work_id":"d1bc633a-40e0-4a4c-91d1-6629fa2ff9d6","year":2017},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.614458Z"},"links":{"citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:c3089b121ce95358072a38ecd405d0b41d7467f3898dda6a8068f6a34aa8dc9f","observation_id":"a8f622a9-0517-42d6-be86-c8c0944c3a7c","resolution":{"observed_at":"2026-08-10T20:04:30.008770Z","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":"1611.06440","last_updated":"2017-06-08T19:53:26Z","snapshot_observed_at":"2026-08-14T21:29:36.502364Z","submitted_at":"2016-11-19T22:48:30Z","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06440","snapshot_observed_at":"2026-08-10T20:04:29.619116Z","title":"Pruning convolutional neural networks for resource efficient inference","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T20:04:29.619116Z"},"links":{"cited_paper":"/paper/1611.06440","citing_paper":"/paper/2501.09464"},"observation_digest":"sha256:2655e3f3109e6091b9ffe8a04072cdd7dce4c12a8401465aa873bcd09603ddb9","observation_id":"c28bc86c-fe93-4ddd-a98c-a421f0e92cc0","resolution":{"observed_at":"2026-08-10T20:04:29.619116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.09464","last_updated":"2025-01-16T10:55:05Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T13:30:26.693527Z","submitted_at":"2025-01-16T10:55:05Z","title":"Pruning for Sparse Diffusion Models based on Gradient Flow"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":16},"total_outbound_references":35},"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 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2501.09464."}