{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OEGRCHMMYACVAWGN5M6FEINICY","short_pith_number":"pith:OEGRCHMM","schema_version":"1.0","canonical_sha256":"710d111d8cc0055058cdeb3c5221a81606e01bddce1c6a32d0d5731b9fddea5d","source":{"kind":"arxiv","id":"2506.21602","version":2},"attestation_state":"computed","paper":{"title":"BiMark: Unbiased Multilayer Watermarking for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"He Zhang, Leo Yu Zhang, Shirui Pan, Xiaoyan Feng, Yanjun Zhang","submitted_at":"2025-06-19T11:08:59Z","abstract_excerpt":"Recent advances in Large Language Models (LLMs) have raised urgent concerns about LLM-generated text authenticity, prompting regulatory demands for reliable identification mechanisms. Although watermarking offers a promising solution, existing approaches struggle to simultaneously achieve three critical requirements: text quality preservation, model-agnostic detection, and message embedding capacity, which are crucial for practical implementation. To achieve these goals, the key challenge lies in balancing the trade-off between text quality preservation and message embedding capacity. To addre"},"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":"2506.21602","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-19T11:08:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c2035b35ca8f1df4a8a467b070d75cc337aa01c7702aedb24e5aa612760147af","abstract_canon_sha256":"fba9126694c13cdd625cd2112edfc4de44d6294dbb081fc6ec83dbae19e66e1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:31.796905Z","signature_b64":"4z9dyht+vC61fKyQFeu402thFCnqL9d7YlzoBQyIzzoeDw2WhBCnDwulw7pn2jkrA46UQqQteRFOdSpC+d8ECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"710d111d8cc0055058cdeb3c5221a81606e01bddce1c6a32d0d5731b9fddea5d","last_reissued_at":"2026-07-05T11:58:31.796480Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:31.796480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BiMark: Unbiased Multilayer Watermarking for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"He Zhang, Leo Yu Zhang, Shirui Pan, Xiaoyan Feng, Yanjun Zhang","submitted_at":"2025-06-19T11:08:59Z","abstract_excerpt":"Recent advances in Large Language Models (LLMs) have raised urgent concerns about LLM-generated text authenticity, prompting regulatory demands for reliable identification mechanisms. Although watermarking offers a promising solution, existing approaches struggle to simultaneously achieve three critical requirements: text quality preservation, model-agnostic detection, and message embedding capacity, which are crucial for practical implementation. To achieve these goals, the key challenge lies in balancing the trade-off between text quality preservation and message embedding capacity. To addre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21602","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/2506.21602/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":"2506.21602","created_at":"2026-07-05T11:58:31.796535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21602v2","created_at":"2026-07-05T11:58:31.796535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21602","created_at":"2026-07-05T11:58:31.796535+00:00"},{"alias_kind":"pith_short_12","alias_value":"OEGRCHMMYACV","created_at":"2026-07-05T11:58:31.796535+00:00"},{"alias_kind":"pith_short_16","alias_value":"OEGRCHMMYACVAWGN","created_at":"2026-07-05T11:58:31.796535+00:00"},{"alias_kind":"pith_short_8","alias_value":"OEGRCHMM","created_at":"2026-07-05T11:58:31.796535+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08400","citing_title":"TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2602.07235","citing_title":"ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11653","citing_title":"Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY","json":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY.json","graph_json":"https://pith.science/api/pith-number/OEGRCHMMYACVAWGN5M6FEINICY/graph.json","events_json":"https://pith.science/api/pith-number/OEGRCHMMYACVAWGN5M6FEINICY/events.json","paper":"https://pith.science/paper/OEGRCHMM"},"agent_actions":{"view_html":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY","download_json":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY.json","view_paper":"https://pith.science/paper/OEGRCHMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21602&json=true","fetch_graph":"https://pith.science/api/pith-number/OEGRCHMMYACVAWGN5M6FEINICY/graph.json","fetch_events":"https://pith.science/api/pith-number/OEGRCHMMYACVAWGN5M6FEINICY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY/action/storage_attestation","attest_author":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY/action/author_attestation","sign_citation":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY/action/citation_signature","submit_replication":"https://pith.science/pith/OEGRCHMMYACVAWGN5M6FEINICY/action/replication_record"}},"created_at":"2026-07-05T11:58:31.796535+00:00","updated_at":"2026-07-05T11:58:31.796535+00:00"}