{"as_of":"2026-08-08T12:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1be9b022c114750e7e5ac06badffc52c99db4450a95f172d73babdb5749e241b","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T00:24:54.156270Z","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":"2026-07-02T02:06:26.134967Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.07703","last_updated":"2022-09-06T02:40:26Z","snapshot_observed_at":"2026-08-04T04:21:16.941116Z","submitted_at":"2022-01-19T16:43:17Z","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","version":2},"cited_work":{"arxiv_id":"2201.07703","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2201.07703","snapshot_observed_at":"2026-07-02T02:06:26.134967Z","title":"Q-ViT: Fully differentiable quantization for vision transformer.arXiv preprint arXiv:2201.07703, 2022","venue":null,"work_id":"d986c6bb-1174-445b-abaf-24848ad5c6ff","year":2022},"citing_paper":{"arxiv_id":"2606.04115","last_updated":"2026-07-14T14:04:32Z","snapshot_observed_at":"2026-08-02T05:34:23.902031Z","submitted_at":"2026-06-02T18:23:20Z","title":"dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T11:20:06.292977Z"},"links":{"cited_paper":"/paper/2201.07703","citing_paper":"/paper/2606.04115"},"observation_digest":"sha256:869561521d944ba610aa418ba7036f12735ee3a6b1a1f0aa60f777ceba60372a","observation_id":"e6ac58b4-c9b3-4bf2-9538-e6cc66c8f84b","resolution":{"observed_at":"2026-07-02T02:06:26.137111Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07703","last_updated":"2022-09-06T02:40:26Z","snapshot_observed_at":"2026-08-04T04:21:16.941116Z","submitted_at":"2022-01-19T16:43:17Z","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.07703","snapshot_observed_at":"2026-07-15T10:56:54.155632Z","title":"Q-ViT: Fully differentiable quantization for vision transformer.arXiv preprint arXiv:2201.07703, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.04115","last_updated":"2026-07-14T14:04:32Z","snapshot_observed_at":"2026-08-02T05:34:23.902031Z","submitted_at":"2026-06-02T18:23:20Z","title":"dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-15T10:56:54.155632Z"},"links":{"cited_paper":"/paper/2201.07703","citing_paper":"/paper/2606.04115"},"observation_digest":"sha256:19ba81036dfb9ccd77a407bf0c50a43248ff2f2548dfd7996500d8c66fa52262","observation_id":"a7f2ba69-2e4f-4f6a-9051-69c02e0e1e90","resolution":{"observed_at":"2026-07-15T10:56:54.155632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07703","last_updated":"2022-09-06T02:40:26Z","snapshot_observed_at":"2026-08-04T04:21:16.941116Z","submitted_at":"2022-01-19T16:43:17Z","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.07703","snapshot_observed_at":"2026-08-01T23:32:35.385258Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15421","last_updated":"2026-07-16T19:51:48Z","snapshot_observed_at":"2026-08-06T20:29:37.238613Z","submitted_at":"2026-07-16T19:51:48Z","title":"qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T23:32:35.385258Z"},"links":{"cited_paper":"/paper/2201.07703","citing_paper":"/paper/2607.15421"},"observation_digest":"sha256:6b43e625a8404a672b3ad9f65ed79da6247a95b840f6a59f86cfff2ca2d20e18","observation_id":"3f3ecb18-5068-452a-a7cc-e4c59bbbeed0","resolution":{"observed_at":"2026-08-01T23:32:35.385258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07703","last_updated":"2022-09-06T02:40:26Z","snapshot_observed_at":"2026-08-04T04:21:16.941116Z","submitted_at":"2022-01-19T16:43:17Z","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.07703","snapshot_observed_at":"2026-08-06T00:24:54.156270Z","title":"Q-ViT: Fully differentiable quantization for vision transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01343","last_updated":"2026-08-02T16:08:44Z","snapshot_observed_at":"2026-08-06T23:26:17.642729Z","submitted_at":"2026-08-02T16:08:44Z","title":"DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T00:24:54.156270Z"},"links":{"cited_paper":"/paper/2201.07703","citing_paper":"/paper/2608.01343"},"observation_digest":"sha256:7642b87fdbdfd54f3dd31569ea8f985728e8f3de9ab17b569434af6e80b5e5b1","observation_id":"481aa083-e58a-491b-a42e-3bab8fdd011d","resolution":{"observed_at":"2026-08-06T00:24:54.156270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2201.07703/citation-record","integrity":"/paper/2201.07703/integrity","json":"/paper/2201.07703/citation-record.json","paper":"/paper/2201.07703"},"outbound":[],"paper":{"arxiv_id":"2201.07703","last_updated":"2022-09-06T02:40:26Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T04:21:16.941116Z","submitted_at":"2022-01-19T16:43:17Z","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2201.07703."}