{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:G74USRANL5VCYDW2GINIDE545Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1552c4e2d8100b9b0de71b04daf0e1d1f846b00942b9650a5f46125ac3664450","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-30T05:55:15Z","title_canon_sha256":"ae3db65d182bda8af103cac01b4a5bcc483e13a66691f754bce8d4c96a494c86"},"schema_version":"1.0","source":{"id":"2407.00600","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.00600","created_at":"2026-07-05T08:38:22Z"},{"alias_kind":"arxiv_version","alias_value":"2407.00600v1","created_at":"2026-07-05T08:38:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00600","created_at":"2026-07-05T08:38:22Z"},{"alias_kind":"pith_short_12","alias_value":"G74USRANL5VC","created_at":"2026-07-05T08:38:22Z"},{"alias_kind":"pith_short_16","alias_value":"G74USRANL5VCYDW2","created_at":"2026-07-05T08:38:22Z"},{"alias_kind":"pith_short_8","alias_value":"G74USRAN","created_at":"2026-07-05T08:38:22Z"}],"graph_snapshots":[{"event_id":"sha256:fe3c4b1b6bc5743192756b96d30e4b1ed36c3f6c9c2fbe439ecf59ce0484c834","target":"graph","created_at":"2026-07-05T08:38:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.00600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Vision-Language Models (LVLMs) have been widely adopted in various applications; however, they exhibit significant gender biases. Existing benchmarks primarily evaluate gender bias at the demographic group level, neglecting individual fairness, which emphasizes equal treatment of similar individuals. This research gap limits the detection of discriminatory behaviors, as individual fairness offers a more granular examination of biases that group fairness may overlook. For the first time, this paper introduces the GenderBias-\\emph{VL} benchmark to evaluate occupation-related gender bias in","authors_text":"Aishan Liu, Dacheng Tao, Jiapeng Li, Jing Shao, Qianjia Cheng, Siyuan Liang, Xianglong Liu, Yisong Xiao, Zhenfei Yin","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-30T05:55:15Z","title":"GenderBias-\\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00600","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9d59f01154ff70f955c72fffd17c7a1697c0f55ccfddf848200e78b7bd4dc756","target":"record","created_at":"2026-07-05T08:38:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1552c4e2d8100b9b0de71b04daf0e1d1f846b00942b9650a5f46125ac3664450","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-30T05:55:15Z","title_canon_sha256":"ae3db65d182bda8af103cac01b4a5bcc483e13a66691f754bce8d4c96a494c86"},"schema_version":"1.0","source":{"id":"2407.00600","kind":"arxiv","version":1}},"canonical_sha256":"37f949440d5f6a2c0eda321a8193bcee1ceffee4007247996fd968f836e18762","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37f949440d5f6a2c0eda321a8193bcee1ceffee4007247996fd968f836e18762","first_computed_at":"2026-07-05T08:38:22.167613Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:22.167613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9KfM5U/jg7gG/gWp3L5BhUMeEIZTg5aVX+MKlEaYp35o9qpqpLlhiyZJWVRrJVM2E43nIbt1w7tFk3VHE11VAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:22.168030Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.00600","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d59f01154ff70f955c72fffd17c7a1697c0f55ccfddf848200e78b7bd4dc756","sha256:fe3c4b1b6bc5743192756b96d30e4b1ed36c3f6c9c2fbe439ecf59ce0484c834"],"state_sha256":"c78e3b88edc452d16248c7cd1bb2d377a539a26b45c95c56c97d911368e3e3da"}