{"as_of":"2026-08-11T10:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c2131129091914afe2834b53798dcd5818e8c7cb7784521e050acf05435e0f7d","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:09:48.229804Z","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":"2026-07-01T19:16:00.843940Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-08-07T04:09:48.229804Z","title":"Dopq- vit: Towards distribution-friendly and outlier-aware post- training quantization for vision transformers.arXiv preprint arXiv:2408.03291, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11543","last_updated":"2025-06-13T07:57:38Z","snapshot_observed_at":"2026-08-09T14:49:22.320087Z","submitted_at":"2025-06-13T07:57:38Z","title":"FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:48.229804Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2506.11543"},"observation_digest":"sha256:ac43886b3fe3cbded564a995e644a3ef9867a7d5b7d961d59c28d0060576f7b9","observation_id":"2cfdb7c2-cf75-4e02-8005-7e0ab6345141","resolution":{"observed_at":"2026-08-07T04:09:48.229804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-08-04T11:31:30.707206Z","title":"Dopq- vit: Towards distribution-friendly and outlier-aware post- training quantization for vision transformers.arXiv preprint arXiv:2408.03291, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.04547","last_updated":"2026-07-08T06:28:12Z","snapshot_observed_at":"2026-08-05T18:52:45.747562Z","submitted_at":"2025-10-06T07:27:46Z","title":"Activation Quantization of Vision Encoders Needs Prefixing Registers","version":5},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T11:31:30.707206Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2510.04547"},"observation_digest":"sha256:38b12c67aed930ef0525bde4c93d9133603e6cdfe68f634bf3b9d4a25a74e72e","observation_id":"7855009e-1a87-4611-85ad-8c52484a4743","resolution":{"observed_at":"2026-08-04T11:31:30.707206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":"2408.03291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-29T01:24:21.058523Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transform- ers","venue":null,"work_id":"c33f205d-0971-497b-b20d-d5d8430a800d","year":2024},"citing_paper":{"arxiv_id":"2602.20309","last_updated":"2026-04-06T23:32:59Z","snapshot_observed_at":"2026-08-11T03:24:57.486480Z","submitted_at":"2026-02-23T19:55:54Z","title":"QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-15T20:20:10.435886Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2602.20309"},"observation_digest":"sha256:cfa05d6fb42578e34a5c2f0d61f6241cb3cf587d6bf8e74d72c540b12bafc130","observation_id":"af056179-2fd3-4ad3-8b8e-110bfee488c9","resolution":{"observed_at":"2026-07-29T01:24:21.058523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":"2408.03291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-29T01:24:21.058523Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transform- ers","venue":null,"work_id":"c33f205d-0971-497b-b20d-d5d8430a800d","year":2024},"citing_paper":{"arxiv_id":"2604.17789","last_updated":"2026-04-21T07:37:48Z","snapshot_observed_at":"2026-08-11T10:21:08.224169Z","submitted_at":"2026-04-20T04:27:28Z","title":"DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T05:59:23.738476Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2604.17789"},"observation_digest":"sha256:abbda8b78a7f6cd268e37684d27bf72ad0b566de5d232b58ad6919fa2cfc8861","observation_id":"85f03042-ff6d-48fa-9dfc-22fca5e08c52","resolution":{"observed_at":"2026-07-29T01:24:21.058523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":"2408.03291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-29T01:24:21.058523Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transform- ers","venue":null,"work_id":"c33f205d-0971-497b-b20d-d5d8430a800d","year":2024},"citing_paper":{"arxiv_id":"2605.01330","last_updated":"2026-05-02T08:49:51Z","snapshot_observed_at":"2026-07-06T23:14:33.653260Z","submitted_at":"2026-05-02T08:49:51Z","title":"Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-09T14:24:54.946996Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2605.01330"},"observation_digest":"sha256:e87eb57b82e347167a61b563c650bd5c41c9afdda727969388b4b458c25f5e51","observation_id":"ef58d8d2-0574-4feb-b14f-992515dc4f89","resolution":{"observed_at":"2026-07-29T01:24:21.058523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":"2408.03291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-29T01:24:21.058523Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transform- ers","venue":null,"work_id":"c33f205d-0971-497b-b20d-d5d8430a800d","year":2024},"citing_paper":{"arxiv_id":"2605.16423","last_updated":"2026-05-14T14:55:46Z","snapshot_observed_at":"2026-07-06T23:27:34.514541Z","submitted_at":"2026-05-14T14:55:46Z","title":"Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-20T20:55:10.360775Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2605.16423"},"observation_digest":"sha256:627b97d54ffaabdddf5139de8bcae616c07398241d43e206de343bd3a390c5c2","observation_id":"cb9a457f-556e-478c-8d76-df93d18d61b4","resolution":{"observed_at":"2026-07-29T01:24:21.058523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":"2408.03291","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-29T01:24:21.058523Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transform- ers","venue":null,"work_id":"c33f205d-0971-497b-b20d-d5d8430a800d","year":2024},"citing_paper":{"arxiv_id":"2605.31124","last_updated":"2026-05-29T10:32:52Z","snapshot_observed_at":"2026-08-09T21:32:18.581828Z","submitted_at":"2026-05-29T10:32:52Z","title":"QVGGT: Post-Training Quantized Visual Geometry Grounded Transformer","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-28T22:54:52.597232Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2605.31124"},"observation_digest":"sha256:ef8f342fe7800c1db5e76aa45f446176ab86aa569acac73c3a8e5593fbd80f6b","observation_id":"e52cee3b-6524-40de-8718-cfff6cecee06","resolution":{"observed_at":"2026-07-29T01:24:21.058523Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03291","snapshot_observed_at":"2026-07-31T02:57:29.309270Z","title":"Dopq-vit: Towards distribution-friendly and outlier-aware post-training quantization for vision transformers.ArXiv, abs/2408.03291,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.28589","last_updated":"2026-07-30T17:43:36Z","snapshot_observed_at":"2026-08-09T14:49:14.944377Z","submitted_at":"2026-07-30T17:43:36Z","title":"MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-31T02:57:29.309270Z"},"links":{"cited_paper":"/paper/2408.03291","citing_paper":"/paper/2607.28589"},"observation_digest":"sha256:49f5671c072a4338de2a0377cca975fa15f0d98023578206b749bb23c031f8ac","observation_id":"26c03923-3228-4e00-b875-e61d40d8f1da","resolution":{"observed_at":"2026-07-31T02:57:29.309270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.03291/citation-record","integrity":"/paper/2408.03291/integrity","json":"/paper/2408.03291/citation-record.json","paper":"/paper/2408.03291"},"outbound":[],"paper":{"arxiv_id":"2408.03291","last_updated":"2026-07-28T09:18:13Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-31T23:52:44.107316Z","submitted_at":"2024-08-06T16:40:04Z","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.03291."}