{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VOMJROPBFLAU4Y4CPRNCDMFMDB","short_pith_number":"pith:VOMJROPB","schema_version":"1.0","canonical_sha256":"ab9898b9e12ac14e63827c5a21b0ac186ad39f31ae68d0c9016cd5e981cf09cd","source":{"kind":"arxiv","id":"2310.00833","version":2},"attestation_state":"computed","paper":{"title":"Necessary and Sufficient Watermark for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Han Bao, Kenta Niwa, Makoto Yamada, Ryoma Sato, Yuki Takezawa","submitted_at":"2023-10-02T00:48:51Z","abstract_excerpt":"In recent years, large language models (LLMs) have achieved remarkable performances in various NLP tasks. They can generate texts that are indistinguishable from those written by humans. Such remarkable performance of LLMs increases their risk of being used for malicious purposes, such as generating fake news articles. Therefore, it is necessary to develop methods for distinguishing texts written by LLMs from those written by humans. Watermarking is one of the most powerful methods for achieving this. Although existing watermarking methods have successfully detected texts generated by LLMs, th"},"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":"2310.00833","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:48:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c01e52e4d9a96d2e84145dae181ec8c1f855689f44afa0c6be8cde78ef6f83a1","abstract_canon_sha256":"93f71ae7c9b6a43667a801fd7c1b456f21f0b37cbc09ad2294a95743dc4474ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:36.891726Z","signature_b64":"l5mWIx2x7joDX+dBjtyfQ8XpstRT5uHy0yY6L+71Yn7fhQ4zTH/5Ix1Gfdwx9VI1o/8DGpUm+Fyme9IDx6irDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab9898b9e12ac14e63827c5a21b0ac186ad39f31ae68d0c9016cd5e981cf09cd","last_reissued_at":"2026-07-05T10:14:36.891219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:36.891219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Necessary and Sufficient Watermark for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Han Bao, Kenta Niwa, Makoto Yamada, Ryoma Sato, Yuki Takezawa","submitted_at":"2023-10-02T00:48:51Z","abstract_excerpt":"In recent years, large language models (LLMs) have achieved remarkable performances in various NLP tasks. They can generate texts that are indistinguishable from those written by humans. Such remarkable performance of LLMs increases their risk of being used for malicious purposes, such as generating fake news articles. Therefore, it is necessary to develop methods for distinguishing texts written by LLMs from those written by humans. Watermarking is one of the most powerful methods for achieving this. Although existing watermarking methods have successfully detected texts generated by LLMs, th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00833","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/2310.00833/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":"2310.00833","created_at":"2026-07-05T10:14:36.891273+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00833v2","created_at":"2026-07-05T10:14:36.891273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00833","created_at":"2026-07-05T10:14:36.891273+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOMJROPBFLAU","created_at":"2026-07-05T10:14:36.891273+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOMJROPBFLAU4Y4C","created_at":"2026-07-05T10:14:36.891273+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOMJROPB","created_at":"2026-07-05T10:14:36.891273+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24163","citing_title":"CORE-BREW: LLR-Based Soft Decoding for Robust Multi-Bit LLM Watermarking","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00348","citing_title":"Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10977","citing_title":"PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2508.11548","citing_title":"Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends","ref_index":135,"is_internal_anchor":false},{"citing_arxiv_id":"2509.20924","citing_title":"RLCracker: Evaluating the Worst-Case Vulnerability of LLM Watermarks with Adaptive RL Attacks","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10977","citing_title":"PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00348","citing_title":"Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08759","citing_title":"Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB","json":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB.json","graph_json":"https://pith.science/api/pith-number/VOMJROPBFLAU4Y4CPRNCDMFMDB/graph.json","events_json":"https://pith.science/api/pith-number/VOMJROPBFLAU4Y4CPRNCDMFMDB/events.json","paper":"https://pith.science/paper/VOMJROPB"},"agent_actions":{"view_html":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB","download_json":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB.json","view_paper":"https://pith.science/paper/VOMJROPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00833&json=true","fetch_graph":"https://pith.science/api/pith-number/VOMJROPBFLAU4Y4CPRNCDMFMDB/graph.json","fetch_events":"https://pith.science/api/pith-number/VOMJROPBFLAU4Y4CPRNCDMFMDB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB/action/storage_attestation","attest_author":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB/action/author_attestation","sign_citation":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB/action/citation_signature","submit_replication":"https://pith.science/pith/VOMJROPBFLAU4Y4CPRNCDMFMDB/action/replication_record"}},"created_at":"2026-07-05T10:14:36.891273+00:00","updated_at":"2026-07-05T10:14:36.891273+00:00"}