{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LSPRX63H73TKXJQQELLNZZVZN3","short_pith_number":"pith:LSPRX63H","canonical_record":{"source":{"id":"2506.20856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T22:01:25Z","cross_cats_sorted":["cs.CL","cs.CR"],"title_canon_sha256":"d9a82153ecfa499b80a9f935c36ab73e25e1069246ac9fcedb5e266ef57ed6ba","abstract_canon_sha256":"2fed42250f083c3c6ed01053e62c4b436c2ecf48ce3173c79eeb67539b36eb21"},"schema_version":"1.0"},"canonical_sha256":"5c9f1bfb67fee6aba61022d6dce6b96ece8b81901f0618e8562a1a5db1ef036a","source":{"kind":"arxiv","id":"2506.20856","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.20856","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"arxiv_version","alias_value":"2506.20856v1","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20856","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_12","alias_value":"LSPRX63H73TK","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_16","alias_value":"LSPRX63H73TKXJQQ","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_8","alias_value":"LSPRX63H","created_at":"2026-07-05T11:27:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LSPRX63H73TKXJQQELLNZZVZN3","target":"record","payload":{"canonical_record":{"source":{"id":"2506.20856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T22:01:25Z","cross_cats_sorted":["cs.CL","cs.CR"],"title_canon_sha256":"d9a82153ecfa499b80a9f935c36ab73e25e1069246ac9fcedb5e266ef57ed6ba","abstract_canon_sha256":"2fed42250f083c3c6ed01053e62c4b436c2ecf48ce3173c79eeb67539b36eb21"},"schema_version":"1.0"},"canonical_sha256":"5c9f1bfb67fee6aba61022d6dce6b96ece8b81901f0618e8562a1a5db1ef036a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:35.116234Z","signature_b64":"XAAjvcTjeKRyF3ONH6CzvpW+mgAYMAyYeL0hpPgcLWOoaBDnDgWFMT6ofGjNRhIRJkwnAcHZt5C/XDXr3IyCDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c9f1bfb67fee6aba61022d6dce6b96ece8b81901f0618e8562a1a5db1ef036a","last_reissued_at":"2026-07-05T11:27:35.115749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:35.115749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.20856","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:27:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HUwbbsGM3Gwzo3nE37tqBx3qnej8UuiUs4fp/DeH8G2CDYSYcuolmPBYr/s44c7ax0PTEExcbWHMOeKqQ5AiAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:34:16.854733Z"},"content_sha256":"aca94de2baf25c2c0c32d5731ef4bb47ef5d16b010e7dc403655eb6bd4d9e014","schema_version":"1.0","event_id":"sha256:aca94de2baf25c2c0c32d5731ef4bb47ef5d16b010e7dc403655eb6bd4d9e014"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LSPRX63H73TKXJQQELLNZZVZN3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CR"],"primary_cat":"cs.LG","authors_text":"Baochun Li, Fei Wang","submitted_at":"2025-06-25T22:01:25Z","abstract_excerpt":"Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method.\n  In this work, we re-examine memorization in fine-tuning and uncover a surprising divergence from prior findings across different fine-tuning strategies. Factors such as model scale and data duplication, which strongly influence memorization in pre-training and full fine-tuning, do not follow the same trend "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20856","kind":"arxiv","version":1},"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/2506.20856/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:27:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vnQ6dzv/MSW3FW5Cwe9/VNer8uvXVZfkSfUvRowcXxACx280LMQp8PJKWfg0hCZ0FSmDSoCNpRMzqk398RWMCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T16:34:16.855625Z"},"content_sha256":"fe0dafaaf82a06d2f8b4c79eade72763f8c02cbf861b9dd5465c1368c3289ba2","schema_version":"1.0","event_id":"sha256:fe0dafaaf82a06d2f8b4c79eade72763f8c02cbf861b9dd5465c1368c3289ba2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LSPRX63H73TKXJQQELLNZZVZN3/bundle.json","state_url":"https://pith.science/pith/LSPRX63H73TKXJQQELLNZZVZN3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LSPRX63H73TKXJQQELLNZZVZN3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T16:34:16Z","links":{"resolver":"https://pith.science/pith/LSPRX63H73TKXJQQELLNZZVZN3","bundle":"https://pith.science/pith/LSPRX63H73TKXJQQELLNZZVZN3/bundle.json","state":"https://pith.science/pith/LSPRX63H73TKXJQQELLNZZVZN3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LSPRX63H73TKXJQQELLNZZVZN3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LSPRX63H73TKXJQQELLNZZVZN3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2fed42250f083c3c6ed01053e62c4b436c2ecf48ce3173c79eeb67539b36eb21","cross_cats_sorted":["cs.CL","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T22:01:25Z","title_canon_sha256":"d9a82153ecfa499b80a9f935c36ab73e25e1069246ac9fcedb5e266ef57ed6ba"},"schema_version":"1.0","source":{"id":"2506.20856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.20856","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"arxiv_version","alias_value":"2506.20856v1","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20856","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_12","alias_value":"LSPRX63H73TK","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_16","alias_value":"LSPRX63H73TKXJQQ","created_at":"2026-07-05T11:27:35Z"},{"alias_kind":"pith_short_8","alias_value":"LSPRX63H","created_at":"2026-07-05T11:27:35Z"}],"graph_snapshots":[{"event_id":"sha256:fe0dafaaf82a06d2f8b4c79eade72763f8c02cbf861b9dd5465c1368c3289ba2","target":"graph","created_at":"2026-07-05T11:27:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.20856/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method.\n  In this work, we re-examine memorization in fine-tuning and uncover a surprising divergence from prior findings across different fine-tuning strategies. Factors such as model scale and data duplication, which strongly influence memorization in pre-training and full fine-tuning, do not follow the same trend ","authors_text":"Baochun Li, Fei Wang","cross_cats":["cs.CL","cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T22:01:25Z","title":"Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20856","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:aca94de2baf25c2c0c32d5731ef4bb47ef5d16b010e7dc403655eb6bd4d9e014","target":"record","created_at":"2026-07-05T11:27:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2fed42250f083c3c6ed01053e62c4b436c2ecf48ce3173c79eeb67539b36eb21","cross_cats_sorted":["cs.CL","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-25T22:01:25Z","title_canon_sha256":"d9a82153ecfa499b80a9f935c36ab73e25e1069246ac9fcedb5e266ef57ed6ba"},"schema_version":"1.0","source":{"id":"2506.20856","kind":"arxiv","version":1}},"canonical_sha256":"5c9f1bfb67fee6aba61022d6dce6b96ece8b81901f0618e8562a1a5db1ef036a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c9f1bfb67fee6aba61022d6dce6b96ece8b81901f0618e8562a1a5db1ef036a","first_computed_at":"2026-07-05T11:27:35.115749Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:27:35.115749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XAAjvcTjeKRyF3ONH6CzvpW+mgAYMAyYeL0hpPgcLWOoaBDnDgWFMT6ofGjNRhIRJkwnAcHZt5C/XDXr3IyCDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:27:35.116234Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.20856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aca94de2baf25c2c0c32d5731ef4bb47ef5d16b010e7dc403655eb6bd4d9e014","sha256:fe0dafaaf82a06d2f8b4c79eade72763f8c02cbf861b9dd5465c1368c3289ba2"],"state_sha256":"49f5f50ac6e0c2e98ea359331bda88cacaa3e9848dca2ba725ddce671bf2b128"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t1wRVRADqFqCvJRguc3EWgmwnZHfzLHRu4mZGqcvWANlA8Li+Ym3/0Ql/ZVgnxjhhekAEZOdwkClaiRfALQKBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T16:34:16.860823Z","bundle_sha256":"cf3fd15527e3746ed58e022284b3a089a5424b4041a5d8cfb2bafdce76c8042d"}}