{"as_of":"2026-08-11T01:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:66fc9ac9b19178c006685ea09ce3949f2ee4241dbbb7f5d25b08315d5f16e08b","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:42:35.252934Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2506.16776/citation-record","integrity":"/paper/2506.16776/integrity","json":"/paper/2506.16776/citation-record.json","paper":"/paper/2506.16776"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:42:43.114752Z","title":"Generative adversarial networks,","venue":null,"work_id":"75b4e11e-577e-4dd2-8b4d-a703615a7cb8","year":2020},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.067152Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:c267dbc701221408e3d5c58811990cb880723cde0d6b2c3f389689acb43986a2","observation_id":"c56500b3-b7b2-4a8c-98a6-579eb691440e","resolution":{"observed_at":"2026-08-06T23:42:43.214748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:42.847666Z","title":"Diffusion models beat gans on image synthesis,","venue":null,"work_id":"b18683df-c2da-4319-aed9-37325f497bc8","year":2021},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.160537Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:1beba83beb8bc49a633cf7d099e6f8fdd2a59dd1fab2646aa4bef9933bc276ab","observation_id":"67537da1-24dc-4935-aa2d-22cdac5a5813","resolution":{"observed_at":"2026-08-06T23:42:42.971958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-06T23:42:32.256845Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.256845Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:f8809fad2d193a368eaa3ebbf5f5933272a2b10749f2f4c0b8510e6431687ebc","observation_id":"189bf7c6-253c-4e4e-a7fc-99909d13f4b3","resolution":{"observed_at":"2026-08-06T23:42:32.256845Z","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":"2026-08-06T23:42:32.409376Z","title":"Score-based generative modeling through stochastic differential equations,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.409376Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:eff999c632d8fe200c2df1d23d334ba0c273b519a188f131d88f43d887bc8d31","observation_id":"bc350214-d451-4700-9ff2-eacbda798e08","resolution":{"observed_at":"2026-08-06T23:42:32.409376Z","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":"2026-08-06T23:42:42.630398Z","title":"Repaint: Inpainting using denoising diffu- sion probabilistic models,","venue":null,"work_id":"541cfdd7-f193-4eea-9f42-55a696a63605","year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.482040Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:43e6c1662a6d76e055c9dd2901fb7740153b8278eec650958c316676ab138e74","observation_id":"c2867b64-3504-4403-a544-c206b79ebc1b","resolution":{"observed_at":"2026-08-06T23:42:42.695039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:42.404745Z","title":"Permutation invariant graph generation via score- based generative modeling,","venue":null,"work_id":"c929bc79-8a5a-4748-b638-9fd2eeec3dd2","year":2020},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.549260Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:9de451138f7114f69e09ac765f56c7bfea5135b0c26114bed92ebaf33ae9ca6b","observation_id":"7daf66fc-bb85-4ef1-9031-13aeeed282d1","resolution":{"observed_at":"2026-08-06T23:42:42.480677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.02923","last_updated":"2022-03-06T09:47:01Z","snapshot_observed_at":"2026-07-06T12:44:48.852327Z","submitted_at":"2022-03-06T09:47:01Z","title":"GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.02923","snapshot_observed_at":"2026-08-06T23:42:32.655110Z","title":"Geodiff: A geometric diffusion model for molecular conformation generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.655110Z"},"links":{"cited_paper":"/paper/2203.02923","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:a6b145edd44ffef576807f45fecefee5ba580b2a48fe34b9aa16aa2a713de481","observation_id":"15c7324a-6289-47f2-be73-5048b08449db","resolution":{"observed_at":"2026-08-06T23:42:32.655110Z","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":"2026-08-06T23:42:42.064754Z","title":"Post-training quantization on diffusion models,","venue":null,"work_id":"cfedaaf9-856a-44fe-bf95-4ae4c5535f57","year":2023},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.808162Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:980b0a28a20100c5e3e2964b9c048dfd2f34b607bfd1ed050de30a688d787e5b","observation_id":"ec4ff8de-7ea9-4941-821f-6af74bcfd45c","resolution":{"observed_at":"2026-08-06T23:42:42.184749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:41.668654Z","title":"Q-diffusion: Quantizing diffusion models,","venue":null,"work_id":"bf4f7394-024d-407c-b554-8ac511dc193a","year":2023},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:32.934754Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:a06391491f7a24d69ab2236007a5761ca4fcb1626898cfa807fd30df6ce5e383","observation_id":"db0c871a-4fc3-4f61-8032-656db439ac85","resolution":{"observed_at":"2026-08-06T23:42:41.844756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.00512","last_updated":"2022-06-07T09:17:35Z","snapshot_observed_at":"2026-08-09T18:51:51.917654Z","submitted_at":"2022-02-01T16:07:25Z","title":"Progressive Distillation for Fast Sampling of Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.00512","snapshot_observed_at":"2026-08-06T23:42:33.054274Z","title":"Progressive distillation for fast sampling of diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.054274Z"},"links":{"cited_paper":"/paper/2202.00512","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:79fa043357fbc9fc59f8fe538753c87206a62f56f67057eefddfc7b360d2458d","observation_id":"bd336daa-7a00-48bb-ba3d-5583e7769951","resolution":{"observed_at":"2026-08-06T23:42:33.054274Z","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":"2026-08-06T23:42:41.296515Z","title":"On distillation of guided diffusion models,","venue":null,"work_id":"3336caef-ebcb-48b5-957e-890c222c5a76","year":2023},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.162094Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:8e4c198838591b7d8a5009652b49d8fe440a5de28374a83e2b4ffcb3b371d32a","observation_id":"e3d2ddbb-1bfe-4962-a484-340daa8455ae","resolution":{"observed_at":"2026-08-06T23:42:41.417528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:43.384942Z","title":"Denoising diffusion prob- abilistic models,","venue":null,"work_id":"cee58e80-03d3-4ba2-9e55-a8da74d8c487","year":2020},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.255142Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:8acbc836a86416c77484a65723d2ae80f5d98b0508ae724c81d83ec259f13611","observation_id":"e084e7b3-a7ab-467d-a8f6-78dec4fdf43b","resolution":{"observed_at":"2026-08-06T23:42:43.504763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","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":"2026-08-06T23:42:33.404825Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.404825Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:51859753d8254df54c7e7923b46157f154a237fea138d4e51ad72dd7e202f045","observation_id":"8951d92a-f79d-45eb-81c3-956180fd4b15","resolution":{"observed_at":"2026-08-06T23:42:33.404825Z","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":"2026-08-06T23:42:40.994794Z","title":"Improved denoising diffusion probabilistic models,","venue":null,"work_id":"c8063d90-880d-4f5e-a89e-56c198d1785d","year":2021},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.584751Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:91f1978e4b23429322e80cab51b09944c40406fa145cb2448f35dd211474c151","observation_id":"9d77e289-48a4-4d74-8de8-f58db35b086f","resolution":{"observed_at":"2026-08-06T23:42:41.192814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:40.688170Z","title":"Structural pruning for diffusion models,","venue":null,"work_id":"edd88d9e-3072-43b1-a0c2-3c069fe16d0a","year":2024},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.652100Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:d4277f6ffbeff914779f24f32f659b0e560a80c07eee6da179b5b1d4b5decc7f","observation_id":"cb6fca8a-85c8-4bc8-9758-47fc51917af0","resolution":{"observed_at":"2026-08-06T23:42:40.852621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:40.424745Z","title":"Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights,","venue":null,"work_id":"d241fccb-8a97-4f17-b85b-a20972c2331e","year":2024},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.747715Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:f864caaebc94455ac2f975e2455e0774b4df3283ea577fa71f355e811505357f","observation_id":"90baa611-439b-4212-86db-bdbc8c50298e","resolution":{"observed_at":"2026-08-06T23:42:40.494703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T23:42:33.849825Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.849825Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:5e1b1b519c66b1cc3ffb8a4f1ebf02ad31e4cff83970cc854dcc8d603951cc4f","observation_id":"0b61cb41-8a3e-4ca2-93fb-174268c7a3d8","resolution":{"observed_at":"2026-08-06T23:42:33.849825Z","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":"2026-08-06T23:42:40.138837Z","title":"Towards effective low-bitwidth convolutional neural networks,","venue":null,"work_id":"ff43c10e-3aae-48ff-a06a-9fea71d92223","year":2018},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.914757Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:a18c094874f7dbcfb4d44fa053f15f3fd8cd52f1cf1c14b4445a5a7cf8a2508e","observation_id":"0ab8105c-b9cd-468e-ab9c-4bb50330cfbb","resolution":{"observed_at":"2026-08-06T23:42:40.254754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:39.795608Z","title":"Effective training of convolutional neural networks with low-bitwidth weights and activations,","venue":null,"work_id":"0f35fdfb-4d8f-4321-9121-5f4e6b36421c","year":2021},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:33.994759Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:431192dd64567a45fdeac9766e077daaeb979e3b1596825a731b1e8a3ce420ba","observation_id":"93b4b2bc-404c-43ad-9313-e965a8521deb","resolution":{"observed_at":"2026-08-06T23:42:39.913619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:39.374693Z","title":"Adaptive loss-aware quantization for multi-bit networks,","venue":null,"work_id":"57e601d0-194e-4237-9790-5bcd877578b2","year":2020},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.114879Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:0df7fe33fc01e9b286174047d756740f5f7001761c6a85f48bd53ee6da91ad4f","observation_id":"6aaa9dd3-f00d-469e-9260-62412d9e00f7","resolution":{"observed_at":"2026-08-06T23:42:39.525476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.12794","last_updated":"2022-06-26T05:54:12Z","snapshot_observed_at":"2026-07-06T13:24:42.403599Z","submitted_at":"2022-06-26T05:54:12Z","title":"CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps","version":1},"cited_work":{"arxiv_id":"2206.12794","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.12794","snapshot_observed_at":"2026-08-06T23:42:36.054754Z","title":"CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps","venue":"cs.CV","work_id":"eae1b980-f598-48b0-aad1-5a152cfb225a","year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.194753Z"},"links":{"cited_paper":"/paper/2206.12794","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:96a87d0c9622d41466ed4cac18c8563d7fecdfc3970b89df507d1ca12eb2b646","observation_id":"73050cd5-5454-4974-ac5e-51efc5f0af48","resolution":{"observed_at":"2026-08-06T23:42:36.193953Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:39.086961Z","title":"Learning to quantize deep networks by optimizing quantization intervals with task loss,","venue":null,"work_id":"f831989f-23dd-4f67-85b8-6906fe9d6831","year":2019},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.294754Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:12a9a962b6c11dc720ba13cdb224b47c1f21dea197954cb3e0f2185081fa3efe","observation_id":"951f6e5b-1a4d-4147-828e-1a2c6d42ceb9","resolution":{"observed_at":"2026-08-06T23:42:39.213669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:38.774752Z","title":"Bit-shrinking: Limiting instantaneous sharpness for improving post-training quantization,","venue":null,"work_id":"6929dad7-2f9d-4f7a-86a4-9ef32c00c312","year":2023},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.374752Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:a8b8fb7cc14caeb0e88e1964b3293559142103c1379c72ba641aa38209296336","observation_id":"aeb45565-028c-4aa7-8192-40fb9488a248","resolution":{"observed_at":"2026-08-06T23:42:38.929888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:38.384752Z","title":"Vari- ational diffusion models,","venue":null,"work_id":"ed631d7c-3578-4c3b-b364-cf8ce14ef98d","year":2021},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.474752Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:0878bcdf862090885e4a60e64a35b649e555d301235afd9781e77b90a72221d7","observation_id":"d75d662a-83d2-4660-89cf-a43a6ab86fd5","resolution":{"observed_at":"2026-08-06T23:42:38.544777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:38.095189Z","title":"Temporal dynamic quantization for diffusion models,","venue":null,"work_id":"ae53dbcd-a9d9-4aff-8db6-4582de91de9f","year":2024},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.574752Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:86a2466945efcc778d6800157271d86ad3796656eaddf24fb6e916ec56aeaf10","observation_id":"4fe087d4-2610-40b1-a476-86b6eb9d3975","resolution":{"observed_at":"2026-08-06T23:42:38.195738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:37.783697Z","title":"Tfmq-dm: Temporal feature maintenance quantization for diffusion models,","venue":null,"work_id":"2537f8ed-a74e-4d9b-a86b-e23253c6071f","year":2024},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.674770Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:d76d7e7e4af61e1739771f5c3c8ea8cc4e1d22ecd156eea061c9b6b26ccc84bb","observation_id":"3b1a4ae5-e97e-42a7-99a2-bd15e532743e","resolution":{"observed_at":"2026-08-06T23:42:37.864754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T23:42:37.536022Z","title":"Up or down? adaptive rounding for post- training quantization,","venue":null,"work_id":"a487251d-b7fb-4bc2-a187-48d2a24fa2fe","year":2020},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.734763Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:2078ffe1e241ce4626972ddf277b4f9e5dbf78cd7ba1b0b4027c9a4491b68c79","observation_id":"c99d7221-b0a9-4730-9854-8fc5cb56b368","resolution":{"observed_at":"2026-08-06T23:42:37.654834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.05426","last_updated":"2021-07-25T09:34:39Z","snapshot_observed_at":"2026-08-10T19:57:21.389202Z","submitted_at":"2021-02-10T13:46:16Z","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.05426","snapshot_observed_at":"2026-08-06T23:42:34.834754Z","title":"Brecq: Pushing the limit of post-training quantization by block reconstruction,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.834754Z"},"links":{"cited_paper":"/paper/2102.05426","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:ba2f4b6650f8a8c1c7ce1d5918e8875e2b3c07576f88cfa41711d244499fc910","observation_id":"57abf048-e823-4285-9eb7-ce4255798b0c","resolution":{"observed_at":"2026-08-06T23:42:34.834754Z","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":"2026-08-06T23:42:37.245061Z","title":"High-resolution image synthesis with latent diffu- sion models,","venue":null,"work_id":"06a6d915-992e-4fc8-942f-d3df90a57a2e","year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:34.944976Z"},"links":{"citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:bb0b43a0a7261186804a6503aa3ebabeeecca4f1d1a450dc868876f4551549b0","observation_id":"0b7f45a3-2ad7-45a5-a526-e72f39c4ba3e","resolution":{"observed_at":"2026-08-06T23:42:37.364902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05740","last_updated":"2023-02-21T11:24:41Z","snapshot_observed_at":"2026-07-06T12:46:41.057346Z","submitted_at":"2022-03-11T04:01:53Z","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05740","snapshot_observed_at":"2026-08-06T23:42:35.025715Z","title":"Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:35.025715Z"},"links":{"cited_paper":"/paper/2203.05740","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:2ed9548f9489e1d330fee4721ac905004535db81a81f2096b6c3ce02b0d6ac5b","observation_id":"f1688ff2-f41e-42d8-bca7-1aa5554578cc","resolution":{"observed_at":"2026-08-06T23:42:35.025715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08153","last_updated":"2020-05-07T03:30:49Z","snapshot_observed_at":"2026-08-10T19:01:35.604774Z","submitted_at":"2019-02-21T17:31:32Z","title":"Learned Step Size Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08153","snapshot_observed_at":"2026-08-06T23:42:35.252934Z","title":"Learned step size quantiza- tion,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:42:35.252934Z"},"links":{"cited_paper":"/paper/1902.08153","citing_paper":"/paper/2506.16776"},"observation_digest":"sha256:4471548b5e1aaa77dcabd80dc0e6dab2f0893ac240f043bf7c1b9aa5462d225f","observation_id":"dccd5cfe-54e6-427a-9325-b1addcea781f","resolution":{"observed_at":"2026-08-06T23:42:35.252934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.16776","last_updated":"2025-06-20T06:43:27Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T23:40:38.128814Z","submitted_at":"2025-06-20T06:43:27Z","title":"PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":21},"total_outbound_references":31},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.16776."}