{"as_of":"2026-08-08T07:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1b4518adb9ca1d6ab159d8f91b1bbb8aa3d9dd8e3010bfb9c392223fb9bb5e2f","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":"2026-08-08T06:32:00.761636+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":"2026-07-31T09:24:20.700996Z","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":"2026-07-04T09:49:44.554563Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":"2405.17814","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-04T09:49:44.554563Z","title":"Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models","venue":null,"work_id":"88fae054-3ab7-42db-9446-9bde0ae49897","year":2024},"citing_paper":{"arxiv_id":"2604.11934","last_updated":"2026-04-13T18:22:50Z","snapshot_observed_at":"2026-07-06T23:00:12.978356Z","submitted_at":"2026-04-13T18:22:50Z","title":"BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T15:33:15.025940Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2604.11934"},"observation_digest":"sha256:8a2056884591c528688b2cc38672138ba99f3b0c86585eacaa496aecf041d3dd","observation_id":"deac33e3-2c54-4db0-8441-64081c5ef7aa","resolution":{"observed_at":"2026-05-11T10:21:00.603342Z","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":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":"2405.17814","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-04T09:49:44.554563Z","title":"Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models","venue":null,"work_id":"88fae054-3ab7-42db-9446-9bde0ae49897","year":2024},"citing_paper":{"arxiv_id":"2606.15127","last_updated":"2026-06-19T06:10:08Z","snapshot_observed_at":"2026-07-06T23:52:32.998232Z","submitted_at":"2026-06-13T05:41:57Z","title":"Beyond Accuracy: Measuring Bias Acknowledgment in Chain-of-Thought Reasoning for Responsible AI Evaluation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T04:43:04.740925Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2606.15127"},"observation_digest":"sha256:b161c743a339a1a579c3a6d838ed57365e94062cae48b506b2933777690895a2","observation_id":"a61174eb-32e5-4925-bd92-5d5fe7ea1178","resolution":{"observed_at":"2026-07-03T16:58:43.418981Z","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":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":"2405.17814","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-04T09:49:44.554563Z","title":"Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models","venue":null,"work_id":"88fae054-3ab7-42db-9446-9bde0ae49897","year":2024},"citing_paper":{"arxiv_id":"2606.22862","last_updated":"2026-06-22T05:10:03Z","snapshot_observed_at":"2026-07-06T23:57:42.632111Z","submitted_at":"2026-06-22T05:10:03Z","title":"Chains That See, Answers That Don't: A Multi-Aspect Evaluation Recipe for Forced Chain-of-Thought on Video-MME","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T09:27:19.695398Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2606.22862"},"observation_digest":"sha256:1fcee5bec705a619fecfaff5f0fdb26e729af37cafd5fdb58f5f6d600d3601a8","observation_id":"c9845bce-560f-4c62-91ac-482443e94818","resolution":{"observed_at":"2026-07-04T09:49:44.556039Z","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":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":"2405.17814","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-04T09:49:44.554563Z","title":"Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models","venue":null,"work_id":"88fae054-3ab7-42db-9446-9bde0ae49897","year":2024},"citing_paper":{"arxiv_id":"2606.31168","last_updated":"2026-06-30T05:58:05Z","snapshot_observed_at":"2026-08-03T22:12:32.827795Z","submitted_at":"2026-06-30T05:58:05Z","title":"Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-07-01T05:50:10.243231Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2606.31168"},"observation_digest":"sha256:b755b2452df8fd962894aab3575cb6858363e4625edeb2ece1362d632a7768d0","observation_id":"e1f182e9-4ff0-4aa9-9d14-ce2c24aa222f","resolution":{"observed_at":"2026-07-01T10:05:41.339836Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-12T09:32:32.684011Z","title":"acl-long.229","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.02586","last_updated":"2026-07-01T05:30:07Z","snapshot_observed_at":"2026-07-31T07:29:48.803345Z","submitted_at":"2026-07-01T05:30:07Z","title":"Auditing the Audit: Five Failure Modes in Benchmark-Validity Audits","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T09:32:32.684011Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2607.02586"},"observation_digest":"sha256:b4acd2a8e67acac00951363ffe43baea9ee308aee9c8e4596da1457530826337","observation_id":"073032d7-3195-4c8d-9845-fbb3ff8f5f94","resolution":{"observed_at":"2026-07-12T09:32:32.684011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17814","last_updated":"2025-02-24T08:49:32Z","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17814","snapshot_observed_at":"2026-07-31T09:24:20.700996Z","title":"arXiv preprint arXiv:2405.17814 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24898","last_updated":"2026-07-27T16:42:36Z","snapshot_observed_at":"2026-08-07T03:41:53.527679Z","submitted_at":"2026-07-27T16:42:36Z","title":"Harm is not Universal: Community-Specific Toxicity Detection is Urgently Needed","version":1},"reference_index":136,"source":"arxiv_source","source_observed_at":"2026-07-31T09:24:20.700996Z"},"links":{"cited_paper":"/paper/2405.17814","citing_paper":"/paper/2607.24898"},"observation_digest":"sha256:c8af3bf3e05e0b5e6984a8f6d40c8009ce30844b63ead848cf5e17a3b62516ce","observation_id":"d06c89b3-12a7-499c-a79f-c856152a41ba","resolution":{"observed_at":"2026-07-31T09:24:20.700996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.17814/citation-record","integrity":"/paper/2405.17814/integrity","json":"/paper/2405.17814/citation-record.json","paper":"/paper/2405.17814"},"outbound":[],"paper":{"arxiv_id":"2405.17814","last_updated":"2025-02-24T08:49:32Z","latest_version":6,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T03:21:47.052030Z","submitted_at":"2024-05-28T04:18:00Z","title":"FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image 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":"2026-08-08T06:32:00.761636+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 6 inbound Pith citation observations for arXiv:2405.17814."}