{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:3EHTQ5GWLP6O53SOD4BBW7ASGM","short_pith_number":"pith:3EHTQ5GW","canonical_record":{"source":{"id":"2403.19738","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T17:54:38Z","cross_cats_sorted":[],"title_canon_sha256":"4078a62f00ef564e96f46e0a74f47dd0cc92c113e846634bf7debd3300998ee8","abstract_canon_sha256":"15eb53a13ccd7e24307ab37b519ba7f738d8b8100a31e5c501018c6f382af9db"},"schema_version":"1.0"},"canonical_sha256":"d90f3874d65bfceeee4e1f021b7c12332a05e63e22ed4b95955f2c1048d3b443","source":{"kind":"arxiv","id":"2403.19738","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19738","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19738v1","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19738","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"3EHTQ5GWLP6O","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"3EHTQ5GWLP6O53SO","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"3EHTQ5GW","created_at":"2026-07-05T08:02:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:3EHTQ5GWLP6O53SOD4BBW7ASGM","target":"record","payload":{"canonical_record":{"source":{"id":"2403.19738","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T17:54:38Z","cross_cats_sorted":[],"title_canon_sha256":"4078a62f00ef564e96f46e0a74f47dd0cc92c113e846634bf7debd3300998ee8","abstract_canon_sha256":"15eb53a13ccd7e24307ab37b519ba7f738d8b8100a31e5c501018c6f382af9db"},"schema_version":"1.0"},"canonical_sha256":"d90f3874d65bfceeee4e1f021b7c12332a05e63e22ed4b95955f2c1048d3b443","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:08.033366Z","signature_b64":"bYpivj2iKco5bVgIkCHcHb4LzU2leYxWOdlpiyxLzWltVLtERYTdzVVndg61+t0rPhAEr0KseliYynOlQlmLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d90f3874d65bfceeee4e1f021b7c12332a05e63e22ed4b95955f2c1048d3b443","last_reissued_at":"2026-07-05T08:02:08.032868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:08.032868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.19738","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5QNSMWxgdipUjLvTafRSWgK8cEpK5PCqu7LjeSjI7Th6EYN6T1neF6VMlmRRcOjstnX///ZSu7nIZB7p1GOWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:37:50.104990Z"},"content_sha256":"6145ddd2f233516b6ac3eca437e93d9b523d85b4189634a7df3a48524367b2bd","schema_version":"1.0","event_id":"sha256:6145ddd2f233516b6ac3eca437e93d9b523d85b4189634a7df3a48524367b2bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:3EHTQ5GWLP6O53SOD4BBW7ASGM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hidir Yesiltepe, Kiymet Akdemir, Pinar Yanardag","submitted_at":"2024-03-28T17:54:38Z","abstract_excerpt":"Diffusion-based text-to-image models have rapidly gained popularity for their ability to generate detailed and realistic images from textual descriptions. However, these models often reflect the biases present in their training data, especially impacting marginalized groups. While prior efforts to debias language models have focused on addressing specific biases, such as racial or gender biases, efforts to tackle intersectional bias have been limited. Intersectional bias refers to the unique form of bias experienced by individuals at the intersection of multiple social identities. Addressing i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19738","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.19738/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DsVtp7XJthJRYLCHwMMXXn7ZmR1J/aPAHamlWG4NHrb/+1nbl23eqiIG8iwMQog3aEzHsAWZD+ESLaPghSjtDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:37:50.105747Z"},"content_sha256":"17d289403a1122e2e3bbb13a5812b88d68aee942dfa03285e52736ed4796a75a","schema_version":"1.0","event_id":"sha256:17d289403a1122e2e3bbb13a5812b88d68aee942dfa03285e52736ed4796a75a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/bundle.json","state_url":"https://pith.science/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T22:37:50Z","links":{"resolver":"https://pith.science/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM","bundle":"https://pith.science/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/bundle.json","state":"https://pith.science/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EHTQ5GWLP6O53SOD4BBW7ASGM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3EHTQ5GWLP6O53SOD4BBW7ASGM","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":"15eb53a13ccd7e24307ab37b519ba7f738d8b8100a31e5c501018c6f382af9db","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T17:54:38Z","title_canon_sha256":"4078a62f00ef564e96f46e0a74f47dd0cc92c113e846634bf7debd3300998ee8"},"schema_version":"1.0","source":{"id":"2403.19738","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19738","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19738v1","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19738","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"3EHTQ5GWLP6O","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"3EHTQ5GWLP6O53SO","created_at":"2026-07-05T08:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"3EHTQ5GW","created_at":"2026-07-05T08:02:08Z"}],"graph_snapshots":[{"event_id":"sha256:17d289403a1122e2e3bbb13a5812b88d68aee942dfa03285e52736ed4796a75a","target":"graph","created_at":"2026-07-05T08:02:08Z","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/2403.19738/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion-based text-to-image models have rapidly gained popularity for their ability to generate detailed and realistic images from textual descriptions. However, these models often reflect the biases present in their training data, especially impacting marginalized groups. While prior efforts to debias language models have focused on addressing specific biases, such as racial or gender biases, efforts to tackle intersectional bias have been limited. Intersectional bias refers to the unique form of bias experienced by individuals at the intersection of multiple social identities. Addressing i","authors_text":"Hidir Yesiltepe, Kiymet Akdemir, Pinar Yanardag","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T17:54:38Z","title":"MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19738","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":"sha256:6145ddd2f233516b6ac3eca437e93d9b523d85b4189634a7df3a48524367b2bd","target":"record","created_at":"2026-07-05T08:02:08Z","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":"15eb53a13ccd7e24307ab37b519ba7f738d8b8100a31e5c501018c6f382af9db","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T17:54:38Z","title_canon_sha256":"4078a62f00ef564e96f46e0a74f47dd0cc92c113e846634bf7debd3300998ee8"},"schema_version":"1.0","source":{"id":"2403.19738","kind":"arxiv","version":1}},"canonical_sha256":"d90f3874d65bfceeee4e1f021b7c12332a05e63e22ed4b95955f2c1048d3b443","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d90f3874d65bfceeee4e1f021b7c12332a05e63e22ed4b95955f2c1048d3b443","first_computed_at":"2026-07-05T08:02:08.032868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:08.032868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bYpivj2iKco5bVgIkCHcHb4LzU2leYxWOdlpiyxLzWltVLtERYTdzVVndg61+t0rPhAEr0KseliYynOlQlmLBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:08.033366Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19738","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6145ddd2f233516b6ac3eca437e93d9b523d85b4189634a7df3a48524367b2bd","sha256:17d289403a1122e2e3bbb13a5812b88d68aee942dfa03285e52736ed4796a75a"],"state_sha256":"90edb047e5a8dd1dbccdc4c00e86ab3bc7a7ed93a069811f7e034ae516c61be6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GcE9UQI8hJdPlznia379FaLr5pOUvFNg2eioHqCclzYwWna6gUgOkP3h1iMpPz2QanGLqVerk2dv2R6Gg2LbDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:37:50.170004Z","bundle_sha256":"ce989870c393baeec90723cb777edf0a23228b370d7ee401667a131c89f3cdcc"}}