{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YXYSWDTXMQF47ZKBHCBRBLC3NL","short_pith_number":"pith:YXYSWDTX","schema_version":"1.0","canonical_sha256":"c5f12b0e77640bcfe541388310ac5b6ad14ce46e19edcb36c9bff3bd36865eaa","source":{"kind":"arxiv","id":"2507.20503","version":1},"attestation_state":"computed","paper":{"title":"Customize Multi-modal RAI Guardrails with Precedent-based predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CY"],"primary_cat":"cs.LG","authors_text":"Cheng-Fu Yang, Christos Christodoulopoulos, Kai-Wei Chang, Rahul Gupta, Thanh Tran, Weitong Ruan","submitted_at":"2025-07-28T03:45:34Z","abstract_excerpt":"A multi-modal guardrail must effectively filter image content based on user-defined policies, identifying material that may be hateful, reinforce harmful stereotypes, contain explicit material, or spread misinformation. Deploying such guardrails in real-world applications, however, poses significant challenges. Users often require varied and highly customizable policies and typically cannot provide abundant examples for each custom policy. Consequently, an ideal guardrail should be scalable to the multiple policies and adaptable to evolving user standards with minimal retraining. Existing fine"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.20503","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-28T03:45:34Z","cross_cats_sorted":["cs.CL","cs.CY"],"title_canon_sha256":"bbddc28e04c5110d958a099389d64353282eeff2b638eea718862584fbe1af8e","abstract_canon_sha256":"ebb542483ae9403b40efc13dafe01e5ae2a3e732f2cd53db21709f6be4e0c9ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:20.397532Z","signature_b64":"RIGpZJsrbkZIgIjCgV17j3pCPBXlVKaq2iZnmbxPkJKjheqedatkb+cbqcLqgHgEsRG81FQpeFmRYfIXkcpRAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5f12b0e77640bcfe541388310ac5b6ad14ce46e19edcb36c9bff3bd36865eaa","last_reissued_at":"2026-07-05T11:44:20.397070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:20.397070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Customize Multi-modal RAI Guardrails with Precedent-based predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CY"],"primary_cat":"cs.LG","authors_text":"Cheng-Fu Yang, Christos Christodoulopoulos, Kai-Wei Chang, Rahul Gupta, Thanh Tran, Weitong Ruan","submitted_at":"2025-07-28T03:45:34Z","abstract_excerpt":"A multi-modal guardrail must effectively filter image content based on user-defined policies, identifying material that may be hateful, reinforce harmful stereotypes, contain explicit material, or spread misinformation. Deploying such guardrails in real-world applications, however, poses significant challenges. Users often require varied and highly customizable policies and typically cannot provide abundant examples for each custom policy. Consequently, an ideal guardrail should be scalable to the multiple policies and adaptable to evolving user standards with minimal retraining. Existing fine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20503","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/2507.20503/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.20503","created_at":"2026-07-05T11:44:20.397129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.20503v1","created_at":"2026-07-05T11:44:20.397129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20503","created_at":"2026-07-05T11:44:20.397129+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXYSWDTXMQF4","created_at":"2026-07-05T11:44:20.397129+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXYSWDTXMQF47ZKB","created_at":"2026-07-05T11:44:20.397129+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXYSWDTX","created_at":"2026-07-05T11:44:20.397129+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL","json":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL.json","graph_json":"https://pith.science/api/pith-number/YXYSWDTXMQF47ZKBHCBRBLC3NL/graph.json","events_json":"https://pith.science/api/pith-number/YXYSWDTXMQF47ZKBHCBRBLC3NL/events.json","paper":"https://pith.science/paper/YXYSWDTX"},"agent_actions":{"view_html":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL","download_json":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL.json","view_paper":"https://pith.science/paper/YXYSWDTX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.20503&json=true","fetch_graph":"https://pith.science/api/pith-number/YXYSWDTXMQF47ZKBHCBRBLC3NL/graph.json","fetch_events":"https://pith.science/api/pith-number/YXYSWDTXMQF47ZKBHCBRBLC3NL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL/action/storage_attestation","attest_author":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL/action/author_attestation","sign_citation":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL/action/citation_signature","submit_replication":"https://pith.science/pith/YXYSWDTXMQF47ZKBHCBRBLC3NL/action/replication_record"}},"created_at":"2026-07-05T11:44:20.397129+00:00","updated_at":"2026-07-05T11:44:20.397129+00:00"}