{"as_of":"2026-08-09T22:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5eb22ba3bf647c03efd7b13d8c2af131bc6a4b57c662dec5b7074f6e5c886abf","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T01:04:45.740471Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T10:17:58.028502Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-08-07T05:44:06.969085Z","title":"Ptqd: Accurate post-training quantization for diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09066","last_updated":"2025-06-08T16:14:37Z","snapshot_observed_at":"2026-08-09T12:21:21.258157Z","submitted_at":"2025-06-08T16:14:37Z","title":"ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:44:06.969085Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2506.09066"},"observation_digest":"sha256:b81bf6b20dafe977ec7f94f6396a4bded65fac6116f78908df590a00f3d1d4db","observation_id":"9c78b94d-0259-4719-8dc9-e21634321c64","resolution":{"observed_at":"2026-08-07T05:44:06.969085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-08-04T19:11:59.515491Z","title":null,"venue":null,"work_id":null,"year":2023},"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":14,"source":"arxiv_source","source_observed_at":"2026-08-04T19:11:59.515491Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2509.09748"},"observation_digest":"sha256:85288ef8f9f9ccf5b60d528c222566bce065f83cec411b9e399bb61e0f2ae9b3","observation_id":"60ad4437-0c07-40e9-9090-5c3a281053dd","resolution":{"observed_at":"2026-08-04T19:11:59.515491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2604.06916","last_updated":"2026-04-08T10:14:47Z","snapshot_observed_at":"2026-07-06T22:55:15.791334Z","submitted_at":"2026-04-08T10:14:47Z","title":"FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-10T18:10:06.994557Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2604.06916"},"observation_digest":"sha256:0f10f2569fc3a9bb4e3d8dff6bdfe993910c08eb25b3e99616faf1797ebb5a51","observation_id":"44511747-15b7-47f0-b8e8-cebfb5981830","resolution":{"observed_at":"2026-05-11T05:21:08.461744Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2604.12668","last_updated":"2026-04-14T12:39:13Z","snapshot_observed_at":"2026-07-06T23:00:51.385974Z","submitted_at":"2026-04-14T12:39:13Z","title":"OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T15:37:25.939058Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2604.12668"},"observation_digest":"sha256:b866c4217ac95ab3711c85e228666142a717244fbbe78930a15748635d3bc0cb","observation_id":"6dc307b1-cc43-4d5b-8300-5df0da0fa5c5","resolution":{"observed_at":"2026-05-11T10:11:02.101361Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2605.20179","last_updated":"2026-05-19T17:59:08Z","snapshot_observed_at":"2026-08-06T13:50:23.608176Z","submitted_at":"2026-05-19T17:59:08Z","title":"TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-20T05:08:07.318040Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2605.20179"},"observation_digest":"sha256:3c1cae4d1831af437e455e3f82e3a50f9b4e2fd8cac5bd794d798e6d908c1663","observation_id":"3ba97499-6cd2-48b3-922a-bb25b555c152","resolution":{"observed_at":"2026-05-20T05:13:03.701766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2605.21072","last_updated":"2026-05-20T11:58:30Z","snapshot_observed_at":"2026-07-06T23:31:32.577242Z","submitted_at":"2026-05-20T11:58:30Z","title":"Q-ARVD: Quantizing Autoregressive Video Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T05:29:04.061943Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2605.21072"},"observation_digest":"sha256:627ac0633f33e93851c9afb185ab3ac38763b3ee0a54b17784b5361e1d8f595c","observation_id":"b6042bcc-01ff-4fc9-a603-dcc6c5036c85","resolution":{"observed_at":"2026-05-21T05:29:39.448589Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2605.23381","last_updated":"2026-05-22T08:50:10Z","snapshot_observed_at":"2026-07-06T23:33:34.409634Z","submitted_at":"2026-05-22T08:50:10Z","title":"VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T04:57:03.913813Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2605.23381"},"observation_digest":"sha256:dada4291cb61cd2153e78523f2392a345314931d2ac85292685136deb8a14fba","observation_id":"fe3e0cad-3cf9-42b2-b1e0-9dfd66e189ba","resolution":{"observed_at":"2026-05-25T05:00:22.567733Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":"2305.10657","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-07-03T10:17:58.028502Z","title":"Ptqd: Accurate post-training quantiza- tion for diffusion models.arXiv preprint arXiv:2305.10657","venue":null,"work_id":"27a6738c-79dd-455a-912a-c66b3a16b35d","year":2023},"citing_paper":{"arxiv_id":"2606.12280","last_updated":"2026-06-10T16:19:49Z","snapshot_observed_at":"2026-07-06T23:51:13.191639Z","submitted_at":"2026-06-10T16:19:49Z","title":"Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T10:05:40.235610Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2606.12280"},"observation_digest":"sha256:3e76504d69d5e0c6c931f80f56037b72de394281df7aa11ba60a957a813152ee","observation_id":"79f5eb79-e22d-4ec8-8204-f529678188a5","resolution":{"observed_at":"2026-07-03T10:17:58.030081Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10657","snapshot_observed_at":"2026-08-08T01:04:45.740471Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03057","last_updated":"2026-08-04T03:14:48Z","snapshot_observed_at":"2026-08-09T01:40:12.831628Z","submitted_at":"2026-08-04T03:14:48Z","title":"TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T01:04:45.740471Z"},"links":{"cited_paper":"/paper/2305.10657","citing_paper":"/paper/2608.03057"},"observation_digest":"sha256:ec253a30aa7e4e7135e90eb2c714a7767a0dc85e868332d72fa0bdb6c0626526","observation_id":"7ac9b83b-64cb-40bc-893c-200112f04bcc","resolution":{"observed_at":"2026-08-08T01:04:45.740471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2305.10657/citation-record","integrity":"/paper/2305.10657/integrity","json":"/paper/2305.10657/citation-record.json","paper":"/paper/2305.10657"},"outbound":[],"paper":{"arxiv_id":"2305.10657","last_updated":"2023-11-01T08:40:41Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T15:29:01.076710Z","submitted_at":"2023-05-18T02:28:42Z","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.10657."}