{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CE3GZ5AKVP72TYZ2ZQKSUVP7J5","short_pith_number":"pith:CE3GZ5AK","canonical_record":{"source":{"id":"2501.00192","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-31T00:06:04Z","cross_cats_sorted":["cs.CL","cs.CY","cs.LG"],"title_canon_sha256":"59e4a5838cd89c3913f03b5b075debc1dc861e02e283b6d46b536d2247038ff0","abstract_canon_sha256":"a4865a4558242d61e4f98e7a71243e4c3ebbffd8f39edc4036a6d6092fb53633"},"schema_version":"1.0"},"canonical_sha256":"11366cf40aabffa9e33acc152a55ff4f5738728e2e84ed304a74058c921f8973","source":{"kind":"arxiv","id":"2501.00192","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00192","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00192v2","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00192","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_12","alias_value":"CE3GZ5AKVP72","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_16","alias_value":"CE3GZ5AKVP72TYZ2","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_8","alias_value":"CE3GZ5AK","created_at":"2026-07-05T10:45:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CE3GZ5AKVP72TYZ2ZQKSUVP7J5","target":"record","payload":{"canonical_record":{"source":{"id":"2501.00192","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-31T00:06:04Z","cross_cats_sorted":["cs.CL","cs.CY","cs.LG"],"title_canon_sha256":"59e4a5838cd89c3913f03b5b075debc1dc861e02e283b6d46b536d2247038ff0","abstract_canon_sha256":"a4865a4558242d61e4f98e7a71243e4c3ebbffd8f39edc4036a6d6092fb53633"},"schema_version":"1.0"},"canonical_sha256":"11366cf40aabffa9e33acc152a55ff4f5738728e2e84ed304a74058c921f8973","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:15.973400Z","signature_b64":"+hG9AGuYls3kihdOxtJCixNf8htsrlMMJMybr/o8RMk8ufhJMD9RzHagCEgROE5vsKT5j+vGgRLMQYpkRUHiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11366cf40aabffa9e33acc152a55ff4f5738728e2e84ed304a74058c921f8973","last_reissued_at":"2026-07-05T10:45:15.972766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:15.972766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.00192","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:45:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5QCqQdU+aN2TYzgbDMGU+95mONYvbgXvZC327ctdE6DdQXZk0VMMj8lVlPaLf7oLHf2qUTN1AAFoW5aetS7+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:59:35.922232Z"},"content_sha256":"0e35bf0b30e98f0c878db52f85c1ae0091c89354da5a4e29e78174943054a792","schema_version":"1.0","event_id":"sha256:0e35bf0b30e98f0c878db52f85c1ae0091c89354da5a4e29e78174943054a792"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CE3GZ5AKVP72TYZ2ZQKSUVP7J5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MLLM-as-a-Judge for Image Safety without Human Labeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CY","cs.LG"],"primary_cat":"cs.CV","authors_text":"Ankit Jain, Dimitris N. Metaxas, Felix Juefei-Xu, Harihar Subramanyam, Jianfa Chen, Li Chen, Ligong Han, Lingjuan Lyu, Nan Jiang, Shiqing Ma, Shiyu Zhao, Shuming Hu, Xiaowen Lin, Zhenting Wang, Zhuowei Li","submitted_at":"2024-12-31T00:06:04Z","abstract_excerpt":"Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00192","kind":"arxiv","version":2},"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/2501.00192/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":"2026-07-05T10:45:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eD/irZxXX3RNk0/s+RPPDarcjwNbw1GogIid6+DGnYu/U7V6ul99u8SzIo4Se89i3hUWC+NY4HhLbmcJccuEDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:59:35.923180Z"},"content_sha256":"2a05b1e76c0e3b1dbd692e06bea764e9d3be1f1357fc94487a871b61b8e48843","schema_version":"1.0","event_id":"sha256:2a05b1e76c0e3b1dbd692e06bea764e9d3be1f1357fc94487a871b61b8e48843"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/bundle.json","state_url":"https://pith.science/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/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":"2026-08-08T20:59:35Z","links":{"resolver":"https://pith.science/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5","bundle":"https://pith.science/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/bundle.json","state":"https://pith.science/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CE3GZ5AKVP72TYZ2ZQKSUVP7J5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CE3GZ5AKVP72TYZ2ZQKSUVP7J5","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":"a4865a4558242d61e4f98e7a71243e4c3ebbffd8f39edc4036a6d6092fb53633","cross_cats_sorted":["cs.CL","cs.CY","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-31T00:06:04Z","title_canon_sha256":"59e4a5838cd89c3913f03b5b075debc1dc861e02e283b6d46b536d2247038ff0"},"schema_version":"1.0","source":{"id":"2501.00192","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00192","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00192v2","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00192","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_12","alias_value":"CE3GZ5AKVP72","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_16","alias_value":"CE3GZ5AKVP72TYZ2","created_at":"2026-07-05T10:45:15Z"},{"alias_kind":"pith_short_8","alias_value":"CE3GZ5AK","created_at":"2026-07-05T10:45:15Z"}],"graph_snapshots":[{"event_id":"sha256:2a05b1e76c0e3b1dbd692e06bea764e9d3be1f1357fc94487a871b61b8e48843","target":"graph","created_at":"2026-07-05T10:45:15Z","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/2501.00192/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however","authors_text":"Ankit Jain, Dimitris N. Metaxas, Felix Juefei-Xu, Harihar Subramanyam, Jianfa Chen, Li Chen, Ligong Han, Lingjuan Lyu, Nan Jiang, Shiqing Ma, Shiyu Zhao, Shuming Hu, Xiaowen Lin, Zhenting Wang, Zhuowei Li","cross_cats":["cs.CL","cs.CY","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-31T00:06:04Z","title":"MLLM-as-a-Judge for Image Safety without Human Labeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00192","kind":"arxiv","version":2},"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":"sha256:0e35bf0b30e98f0c878db52f85c1ae0091c89354da5a4e29e78174943054a792","target":"record","created_at":"2026-07-05T10:45:15Z","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":"a4865a4558242d61e4f98e7a71243e4c3ebbffd8f39edc4036a6d6092fb53633","cross_cats_sorted":["cs.CL","cs.CY","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-31T00:06:04Z","title_canon_sha256":"59e4a5838cd89c3913f03b5b075debc1dc861e02e283b6d46b536d2247038ff0"},"schema_version":"1.0","source":{"id":"2501.00192","kind":"arxiv","version":2}},"canonical_sha256":"11366cf40aabffa9e33acc152a55ff4f5738728e2e84ed304a74058c921f8973","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"11366cf40aabffa9e33acc152a55ff4f5738728e2e84ed304a74058c921f8973","first_computed_at":"2026-07-05T10:45:15.972766Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:45:15.972766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+hG9AGuYls3kihdOxtJCixNf8htsrlMMJMybr/o8RMk8ufhJMD9RzHagCEgROE5vsKT5j+vGgRLMQYpkRUHiBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:45:15.973400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.00192","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0e35bf0b30e98f0c878db52f85c1ae0091c89354da5a4e29e78174943054a792","sha256:2a05b1e76c0e3b1dbd692e06bea764e9d3be1f1357fc94487a871b61b8e48843"],"state_sha256":"dd3b61d81971b6628a4aa1c412b838607d4b446575a2aa49c6f5d704090cc65a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9oQR1feDpURFSFAGgA5b1jQPddv5ox1pbFkCBlBfelTtyP3f5D9p5xBfX3ZzhmZ4M8q+WGtTyG4f2MFeDczSDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:59:35.928698Z","bundle_sha256":"3f231793e52860f350fa4c7921f7773f83ed6407b7893ac4dc3e3fb6d51839cc"}}