{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G7AHZGHPDFKVFC6YFSWLR2KDDH","short_pith_number":"pith:G7AHZGHP","schema_version":"1.0","canonical_sha256":"37c07c98ef1955528bd82cacb8e94319c9ac70b7b269b7b4792ea428b5dfa65a","source":{"kind":"arxiv","id":"2403.04808","version":3},"attestation_state":"computed","paper":{"title":"WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CR","authors_text":"Eva Giboulot, Teddy Furon","submitted_at":"2024-03-06T10:55:30Z","abstract_excerpt":"Watermarking is a technical means to dissuade malfeasant usage of Large Language Models. This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM. Its new design leaves the LLM untouched (no modification of the weights, logits, temperature, or sampling technique). WaterMax balances robustness and complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness. Its performance is both theoretically proven and exper"},"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":"2403.04808","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-03-06T10:55:30Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"4ed5012f948756fd0a7a635b37c47f6ae17f17020eed7439b47a5d7832abb338","abstract_canon_sha256":"4066476cd663e9b5e30ee1801c411e2464c3ef36c5c1ebdfc7cc0112fe700e7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:16.450552Z","signature_b64":"cl4UtDfdhfRJB7g2ttwgSKfZTYEj5HNCGxtErUi0euEfpo4Fo7OHhC6XUiskIEGwM44AeVKlEe0o8De5qwDGBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37c07c98ef1955528bd82cacb8e94319c9ac70b7b269b7b4792ea428b5dfa65a","last_reissued_at":"2026-07-05T09:22:16.450073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:16.450073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CR","authors_text":"Eva Giboulot, Teddy Furon","submitted_at":"2024-03-06T10:55:30Z","abstract_excerpt":"Watermarking is a technical means to dissuade malfeasant usage of Large Language Models. This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM. Its new design leaves the LLM untouched (no modification of the weights, logits, temperature, or sampling technique). WaterMax balances robustness and complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness. Its performance is both theoretically proven and exper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.04808","kind":"arxiv","version":3},"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/2403.04808/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":"2403.04808","created_at":"2026-07-05T09:22:16.450131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.04808v3","created_at":"2026-07-05T09:22:16.450131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.04808","created_at":"2026-07-05T09:22:16.450131+00:00"},{"alias_kind":"pith_short_12","alias_value":"G7AHZGHPDFKV","created_at":"2026-07-05T09:22:16.450131+00:00"},{"alias_kind":"pith_short_16","alias_value":"G7AHZGHPDFKVFC6Y","created_at":"2026-07-05T09:22:16.450131+00:00"},{"alias_kind":"pith_short_8","alias_value":"G7AHZGHP","created_at":"2026-07-05T09:22:16.450131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12456","citing_title":"TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2510.18333","citing_title":"Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12456","citing_title":"TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08964","citing_title":"Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05443","citing_title":"SLAM: Structural Linguistic Activation Marking for Language Models","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH","json":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH.json","graph_json":"https://pith.science/api/pith-number/G7AHZGHPDFKVFC6YFSWLR2KDDH/graph.json","events_json":"https://pith.science/api/pith-number/G7AHZGHPDFKVFC6YFSWLR2KDDH/events.json","paper":"https://pith.science/paper/G7AHZGHP"},"agent_actions":{"view_html":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH","download_json":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH.json","view_paper":"https://pith.science/paper/G7AHZGHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.04808&json=true","fetch_graph":"https://pith.science/api/pith-number/G7AHZGHPDFKVFC6YFSWLR2KDDH/graph.json","fetch_events":"https://pith.science/api/pith-number/G7AHZGHPDFKVFC6YFSWLR2KDDH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH/action/storage_attestation","attest_author":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH/action/author_attestation","sign_citation":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH/action/citation_signature","submit_replication":"https://pith.science/pith/G7AHZGHPDFKVFC6YFSWLR2KDDH/action/replication_record"}},"created_at":"2026-07-05T09:22:16.450131+00:00","updated_at":"2026-07-05T09:22:16.450131+00:00"}