{"as_of":"2026-08-12T23:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e44cb2bca43cc504571785035c62ffbd4e001415a97a15c022e8a815f90daf76","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T16:16:26.318406Z","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":"2026-05-13T17:38:02.604390Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-08-12T16:16:26.318406Z","title":"Mixdq: Memory-efficient few-step text-to-image dif- fusion models with metric-decoupled mixed precision quan- tization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.13787","last_updated":"2025-08-21T05:25:55Z","snapshot_observed_at":"2026-08-12T23:03:58.203776Z","submitted_at":"2024-11-21T02:18:06Z","title":"Adaptive Routing of Text-to-Image Generation Requests Between Large Cloud Model and Light-Weight Edge Model","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T16:16:26.318406Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2411.13787"},"observation_digest":"sha256:4188e3f228dfc64a6cd51a59d85612ca53405b497d3bd4db88b3e2abc850e2a8","observation_id":"a1c1490e-c7a0-4e41-a352-43014e1ae1d5","resolution":{"observed_at":"2026-08-12T16:16:26.318406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-08-11T04:39:36.771152Z","title":"Mixdq: Memory-efficient few-step text-to-image dif- fusion models with metric-decoupled mixed precision quan- tization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.18653","last_updated":"2024-12-24T19:00:02Z","snapshot_observed_at":"2026-08-11T04:33:55.415799Z","submitted_at":"2024-12-24T19:00:02Z","title":"1.58-bit FLUX","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T04:39:36.771152Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2412.18653"},"observation_digest":"sha256:24418d12b00a181d0f37600a5c06cc06d7cd8d9f7552a6f3244e35f9872e0c03","observation_id":"be355e0f-dbd9-43ba-b537-1d5b90172431","resolution":{"observed_at":"2026-08-11T04:39:36.771152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-08-10T22:58:48.229800Z","title":"Mixdq: Memory-efficient few-step text-to-image dif- fusion models with metric-decoupled mixed precision quan- tization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00375","last_updated":"2024-12-31T09:56:40Z","snapshot_observed_at":"2026-08-12T14:26:38.597825Z","submitted_at":"2024-12-31T09:56:40Z","title":"Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T22:58:48.229800Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2501.00375"},"observation_digest":"sha256:3f5e1d4b7bd5090167cc4eaabcf806e642a6e7987a206b076c234ddcdf0a2737","observation_id":"9a33989e-afef-4c28-870d-21321738902e","resolution":{"observed_at":"2026-08-10T22:58:48.229800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-08-10T21:42:07.111364Z","title":"Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.04304","last_updated":"2025-02-12T10:49:40Z","snapshot_observed_at":"2026-08-12T09:40:56.074534Z","submitted_at":"2025-01-08T06:30:31Z","title":"DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:42:07.111364Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2501.04304"},"observation_digest":"sha256:5bb229a55d771d847f84aa29add8b6a980125cc1b4edefc2ad1d068ad0e49062","observation_id":"2a57c016-2f90-4eb0-a481-e0c7cf9075f4","resolution":{"observed_at":"2026-08-10T21:42:07.111364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":"2405.17873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization","venue":null,"work_id":"eabaed74-23ae-4897-a21b-1dc0dbbadd7c","year":2024},"citing_paper":{"arxiv_id":"2604.04013","last_updated":"2026-04-05T08:04:39Z","snapshot_observed_at":"2026-08-11T09:45:12.224702Z","submitted_at":"2026-04-05T08:04:39Z","title":"RUQuant: Towards Refining Uniform Quantization for Large Language Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-13T17:36:53.861234Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2604.04013"},"observation_digest":"sha256:77989ffded5c5207ebbad12dcc3a89ed99c7a570f891156c707d942cebab567a","observation_id":"4cbc07f5-ae24-4ee1-9617-b467a2db9455","resolution":{"observed_at":"2026-05-13T17:38:02.607204Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","version":2},"cited_work":{"arxiv_id":"2405.17873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17873","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mixdq: Memory-efficient few-step text-to-image diffusion models with metric-decoupled mixed precision quantization","venue":null,"work_id":"eabaed74-23ae-4897-a21b-1dc0dbbadd7c","year":2024},"citing_paper":{"arxiv_id":"2604.18117","last_updated":"2026-04-20T11:37:10Z","snapshot_observed_at":"2026-08-11T05:19:08.196658Z","submitted_at":"2026-04-20T11:37:10Z","title":"LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T05:34:16.191828Z"},"links":{"cited_paper":"/paper/2405.17873","citing_paper":"/paper/2604.18117"},"observation_digest":"sha256:69957a4aaded9da8f0ba0348a3f91f8e26fd45736bf2f7aee3a5e5e286256c94","observation_id":"494affbc-c4a3-4af7-b62c-4c81c0e38169","resolution":{"observed_at":"2026-05-10T05:36:01.609669Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.17873/citation-record","integrity":"/paper/2405.17873/integrity","json":"/paper/2405.17873/citation-record.json","paper":"/paper/2405.17873"},"outbound":[],"paper":{"arxiv_id":"2405.17873","last_updated":"2024-05-30T01:51:10Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T16:41:06.141639Z","submitted_at":"2024-05-28T06:50:58Z","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.17873."}