{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2ZIIXDEDOAX3EUYLISWK6ULZY2","short_pith_number":"pith:2ZIIXDED","schema_version":"1.0","canonical_sha256":"d6508b8c83702fb2530b44acaf5179c6b582a660beadb0495a1463acb2985e2f","source":{"kind":"arxiv","id":"2509.05316","version":2},"attestation_state":"computed","paper":{"title":"Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Lucia Passaro, Praveen Bushipaka, Tommaso Cucinotta","submitted_at":"2025-08-29T19:25:52Z","abstract_excerpt":"A conventional LLM Unlearning setting consists of two subsets -\"forget\" and \"retain\", with the objectives of removing the undesired knowledge from the forget set while preserving the remaining knowledge from the retain. In privacy-focused unlearning research, a retain set is often further divided into neighbor sets, containing either directly or indirectly connected to the forget targets; and augmented by a general-knowledge set. A common practice in existing benchmarks is to employ only a single neighbor set, with general knowledge which fails to reflect the real-world data complexities and r"},"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":"2509.05316","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-29T19:25:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e584c1319a67ae0a80322b29f1360adbb1ab039e1ee8864564fcebfcb0cb9bb7","abstract_canon_sha256":"bdcaad56635bacb74b9c24487e69a83d9c324b6031777cc23ead2294c836713a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:05:02.853625Z","signature_b64":"gz1isNQqkf944XCiYLfsLEHBrXkODe10eG0f9aZj0lr5yAFZyfO4jPued2f6xqVYlfPZNlBZWZGHhL5HmWuyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6508b8c83702fb2530b44acaf5179c6b582a660beadb0495a1463acb2985e2f","last_reissued_at":"2026-06-08T01:05:02.853009Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:05:02.853009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Lucia Passaro, Praveen Bushipaka, Tommaso Cucinotta","submitted_at":"2025-08-29T19:25:52Z","abstract_excerpt":"A conventional LLM Unlearning setting consists of two subsets -\"forget\" and \"retain\", with the objectives of removing the undesired knowledge from the forget set while preserving the remaining knowledge from the retain. In privacy-focused unlearning research, a retain set is often further divided into neighbor sets, containing either directly or indirectly connected to the forget targets; and augmented by a general-knowledge set. A common practice in existing benchmarks is to employ only a single neighbor set, with general knowledge which fails to reflect the real-world data complexities and r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05316","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/2509.05316/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":"2509.05316","created_at":"2026-06-08T01:05:02.853074+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05316v2","created_at":"2026-06-08T01:05:02.853074+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05316","created_at":"2026-06-08T01:05:02.853074+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZIIXDEDOAX3","created_at":"2026-06-08T01:05:02.853074+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZIIXDEDOAX3EUYL","created_at":"2026-06-08T01:05:02.853074+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZIIXDED","created_at":"2026-06-08T01:05:02.853074+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/2ZIIXDEDOAX3EUYLISWK6ULZY2","json":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2.json","graph_json":"https://pith.science/api/pith-number/2ZIIXDEDOAX3EUYLISWK6ULZY2/graph.json","events_json":"https://pith.science/api/pith-number/2ZIIXDEDOAX3EUYLISWK6ULZY2/events.json","paper":"https://pith.science/paper/2ZIIXDED"},"agent_actions":{"view_html":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2","download_json":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2.json","view_paper":"https://pith.science/paper/2ZIIXDED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05316&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZIIXDEDOAX3EUYLISWK6ULZY2/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZIIXDEDOAX3EUYLISWK6ULZY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2/action/storage_attestation","attest_author":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2/action/author_attestation","sign_citation":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2/action/citation_signature","submit_replication":"https://pith.science/pith/2ZIIXDEDOAX3EUYLISWK6ULZY2/action/replication_record"}},"created_at":"2026-06-08T01:05:02.853074+00:00","updated_at":"2026-06-08T01:05:02.853074+00:00"}