{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RG3CQAZEX2M5A25MLLGNXFWWFJ","short_pith_number":"pith:RG3CQAZE","schema_version":"1.0","canonical_sha256":"89b6280324be99d06bac5accdb96d62a6dd69c04b827c8349fee0e4730a59f4c","source":{"kind":"arxiv","id":"2411.19563","version":2},"attestation_state":"computed","paper":{"title":"Ensemble Watermarks for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Georg Niess, Roman Kern","submitted_at":"2024-11-29T09:18:32Z","abstract_excerpt":"As large language models (LLMs) reach human-like fluency, reliably distinguishing AI-generated text from human authorship becomes increasingly difficult. While watermarks already exist for LLMs, they often lack flexibility and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-feature method for generating watermarks that combines multiple distinct watermark features into an ensemble watermark. Concretely, we combine acrostica and sensorimotor norms with the established red-green watermark to achieve a 98% detection rate. After a paraphrasing attack, the pe"},"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":"2411.19563","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-29T09:18:32Z","cross_cats_sorted":[],"title_canon_sha256":"9ed3c86415827315321116dcb16f08f707d1c02560c83aa873e9a42ad95b538c","abstract_canon_sha256":"0128c41f2f942a6a6ac957063d8bd0b34e6d9b7cedd521031d9dfa649a36733a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:34.277825Z","signature_b64":"xBAop7qjQ93ORr5ZHEhbbo/2RsVEM+XkElb+SXXG0GEjxAdi9jGrI0jegb7Mi5Rvx3WxTeobHPOk34rynMctCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89b6280324be99d06bac5accdb96d62a6dd69c04b827c8349fee0e4730a59f4c","last_reissued_at":"2026-07-05T11:22:34.277268Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:34.277268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble Watermarks for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Georg Niess, Roman Kern","submitted_at":"2024-11-29T09:18:32Z","abstract_excerpt":"As large language models (LLMs) reach human-like fluency, reliably distinguishing AI-generated text from human authorship becomes increasingly difficult. While watermarks already exist for LLMs, they often lack flexibility and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-feature method for generating watermarks that combines multiple distinct watermark features into an ensemble watermark. Concretely, we combine acrostica and sensorimotor norms with the established red-green watermark to achieve a 98% detection rate. After a paraphrasing attack, the pe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19563","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/2411.19563/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":"2411.19563","created_at":"2026-07-05T11:22:34.277327+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19563v2","created_at":"2026-07-05T11:22:34.277327+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19563","created_at":"2026-07-05T11:22:34.277327+00:00"},{"alias_kind":"pith_short_12","alias_value":"RG3CQAZEX2M5","created_at":"2026-07-05T11:22:34.277327+00:00"},{"alias_kind":"pith_short_16","alias_value":"RG3CQAZEX2M5A25M","created_at":"2026-07-05T11:22:34.277327+00:00"},{"alias_kind":"pith_short_8","alias_value":"RG3CQAZE","created_at":"2026-07-05T11:22:34.277327+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/RG3CQAZEX2M5A25MLLGNXFWWFJ","json":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ.json","graph_json":"https://pith.science/api/pith-number/RG3CQAZEX2M5A25MLLGNXFWWFJ/graph.json","events_json":"https://pith.science/api/pith-number/RG3CQAZEX2M5A25MLLGNXFWWFJ/events.json","paper":"https://pith.science/paper/RG3CQAZE"},"agent_actions":{"view_html":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ","download_json":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ.json","view_paper":"https://pith.science/paper/RG3CQAZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19563&json=true","fetch_graph":"https://pith.science/api/pith-number/RG3CQAZEX2M5A25MLLGNXFWWFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/RG3CQAZEX2M5A25MLLGNXFWWFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ/action/storage_attestation","attest_author":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ/action/author_attestation","sign_citation":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ/action/citation_signature","submit_replication":"https://pith.science/pith/RG3CQAZEX2M5A25MLLGNXFWWFJ/action/replication_record"}},"created_at":"2026-07-05T11:22:34.277327+00:00","updated_at":"2026-07-05T11:22:34.277327+00:00"}