{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ET27ZIUYBLZX7GK752ZPVZ5HAV","short_pith_number":"pith:ET27ZIUY","canonical_record":{"source":{"id":"2405.14705","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T15:39:43Z","cross_cats_sorted":[],"title_canon_sha256":"76100e136a22b7a1a55530b3d47a029f5cb073f702cdb2df5d54c791898f87a5","abstract_canon_sha256":"8836036c7fccaf536a6089a1b1e63f5eb9925a521dd403ca6f118953e2ae3595"},"schema_version":"1.0"},"canonical_sha256":"24f5fca2980af37f995feeb2fae7a7054555a2901e29308bdb03c4c5f7ba9c2e","source":{"kind":"arxiv","id":"2405.14705","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.14705","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"arxiv_version","alias_value":"2405.14705v1","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14705","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_12","alias_value":"ET27ZIUYBLZX","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_16","alias_value":"ET27ZIUYBLZX7GK7","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_8","alias_value":"ET27ZIUY","created_at":"2026-07-05T08:22:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ET27ZIUYBLZX7GK752ZPVZ5HAV","target":"record","payload":{"canonical_record":{"source":{"id":"2405.14705","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T15:39:43Z","cross_cats_sorted":[],"title_canon_sha256":"76100e136a22b7a1a55530b3d47a029f5cb073f702cdb2df5d54c791898f87a5","abstract_canon_sha256":"8836036c7fccaf536a6089a1b1e63f5eb9925a521dd403ca6f118953e2ae3595"},"schema_version":"1.0"},"canonical_sha256":"24f5fca2980af37f995feeb2fae7a7054555a2901e29308bdb03c4c5f7ba9c2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:26.684406Z","signature_b64":"XZvSBEDjTLruz6CeLjUjiMBaufsDd+YNdv2cj/untlLs1dUkOFEkB+OyPUF0E0fAA2lbFlh5KXWB5uUUJVncBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24f5fca2980af37f995feeb2fae7a7054555a2901e29308bdb03c4c5f7ba9c2e","last_reissued_at":"2026-07-05T08:22:26.683989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:26.683989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.14705","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":"2026-07-05T08:22:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8bZhJ3SEJUFg3W/ob9PENEQoYCLqc3EdPFVwa+4WRQ6PVY5Kbqmjs/+4Ybz192IjNgkBFBr92nWEARV5NSEqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:08:25.551559Z"},"content_sha256":"d7656bd10c000ed39949c7890258e286fe437d9ebad2d831352142f71451a318","schema_version":"1.0","event_id":"sha256:d7656bd10c000ed39949c7890258e286fe437d9ebad2d831352142f71451a318"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ET27ZIUYBLZX7GK752ZPVZ5HAV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Multi-dimensional Human Preference for Text-to-Image Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohan Wang, Di Zhang, Junqiang Wu, Sixian Zhang, Tingting Gao, Yan Li, Zhongyuan Wang","submitted_at":"2024-05-23T15:39:43Z","abstract_excerpt":"Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images, they reduce the rich tapestry of human preference to a single overall score. However, the preference results vary when humans evaluate images with different aspects. Therefore, to learn the multi-dimensional human preferences, we propose the Multi-dimensional Preference Score (MPS), the first multi-dimensional preference scoring model for the evaluation of text-to-image mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14705","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/2405.14705/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":"2026-07-05T08:22:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aiL63pHQ0avGJ/3GxAFjVTP/l8ykiQOHkjBLvCQuH/T51ZSDPR4UDbk5XGBGJ/J7kFOPBBgIeMn25AB8uz53AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:08:25.552076Z"},"content_sha256":"6948c7346c7e8f9a165b23d5d75b7728a30b983655cb841ef3662a0279ee9a8e","schema_version":"1.0","event_id":"sha256:6948c7346c7e8f9a165b23d5d75b7728a30b983655cb841ef3662a0279ee9a8e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/bundle.json","state_url":"https://pith.science/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/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":"2026-08-08T20:08:25Z","links":{"resolver":"https://pith.science/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV","bundle":"https://pith.science/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/bundle.json","state":"https://pith.science/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ET27ZIUYBLZX7GK752ZPVZ5HAV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ET27ZIUYBLZX7GK752ZPVZ5HAV","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":"8836036c7fccaf536a6089a1b1e63f5eb9925a521dd403ca6f118953e2ae3595","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T15:39:43Z","title_canon_sha256":"76100e136a22b7a1a55530b3d47a029f5cb073f702cdb2df5d54c791898f87a5"},"schema_version":"1.0","source":{"id":"2405.14705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.14705","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"arxiv_version","alias_value":"2405.14705v1","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14705","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_12","alias_value":"ET27ZIUYBLZX","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_16","alias_value":"ET27ZIUYBLZX7GK7","created_at":"2026-07-05T08:22:26Z"},{"alias_kind":"pith_short_8","alias_value":"ET27ZIUY","created_at":"2026-07-05T08:22:26Z"}],"graph_snapshots":[{"event_id":"sha256:6948c7346c7e8f9a165b23d5d75b7728a30b983655cb841ef3662a0279ee9a8e","target":"graph","created_at":"2026-07-05T08:22:26Z","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/2405.14705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images, they reduce the rich tapestry of human preference to a single overall score. However, the preference results vary when humans evaluate images with different aspects. Therefore, to learn the multi-dimensional human preferences, we propose the Multi-dimensional Preference Score (MPS), the first multi-dimensional preference scoring model for the evaluation of text-to-image mod","authors_text":"Bohan Wang, Di Zhang, Junqiang Wu, Sixian Zhang, Tingting Gao, Yan Li, Zhongyuan Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T15:39:43Z","title":"Learning Multi-dimensional Human Preference for Text-to-Image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14705","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:d7656bd10c000ed39949c7890258e286fe437d9ebad2d831352142f71451a318","target":"record","created_at":"2026-07-05T08:22:26Z","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":"8836036c7fccaf536a6089a1b1e63f5eb9925a521dd403ca6f118953e2ae3595","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-23T15:39:43Z","title_canon_sha256":"76100e136a22b7a1a55530b3d47a029f5cb073f702cdb2df5d54c791898f87a5"},"schema_version":"1.0","source":{"id":"2405.14705","kind":"arxiv","version":1}},"canonical_sha256":"24f5fca2980af37f995feeb2fae7a7054555a2901e29308bdb03c4c5f7ba9c2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24f5fca2980af37f995feeb2fae7a7054555a2901e29308bdb03c4c5f7ba9c2e","first_computed_at":"2026-07-05T08:22:26.683989Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:22:26.683989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XZvSBEDjTLruz6CeLjUjiMBaufsDd+YNdv2cj/untlLs1dUkOFEkB+OyPUF0E0fAA2lbFlh5KXWB5uUUJVncBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:22:26.684406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.14705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7656bd10c000ed39949c7890258e286fe437d9ebad2d831352142f71451a318","sha256:6948c7346c7e8f9a165b23d5d75b7728a30b983655cb841ef3662a0279ee9a8e"],"state_sha256":"28f39ad2ee52c7bee34a7f2179222c072381e930d35910dc2dc2ec3c8f076914"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pIMHRx4i12l6knE73XEbqx8T2a/lehsuiLSdSuH6753VntpF6vG84oVM/rUAq3HBELl2jWaAgCx9ZBZ9VQSyCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:08:25.556315Z","bundle_sha256":"d6e3ae13fe39253b039de3aefd695adaf1386f660cf1e182fdc34d6814c101af"}}