{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A7JVKW4M6CQRASW7AWD5NITPDD","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":"32ddc87d287cb64968e9f65e5f1197426f9254e13c04d44c14c428b35e497735","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-12T19:26:35Z","title_canon_sha256":"b4cab41d1878ab8da391aac6d12858f0fdd5c44b997d5f5e62dc22891cb15b9f"},"schema_version":"1.0","source":{"id":"2406.08607","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.08607","created_at":"2026-07-05T08:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"2406.08607v1","created_at":"2026-07-05T08:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.08607","created_at":"2026-07-05T08:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"A7JVKW4M6CQR","created_at":"2026-07-05T08:31:19Z"},{"alias_kind":"pith_short_16","alias_value":"A7JVKW4M6CQRASW7","created_at":"2026-07-05T08:31:19Z"},{"alias_kind":"pith_short_8","alias_value":"A7JVKW4M","created_at":"2026-07-05T08:31:19Z"}],"graph_snapshots":[{"event_id":"sha256:30ef9efbf35d3623a5b22b0140a2821630061eb45f413ef03bbad12bc248c7d8","target":"graph","created_at":"2026-07-05T08:31:19Z","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/2406.08607/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As Large Language Models (LLMs) demonstrate extensive capability in learning from documents, LLM unlearning becomes an increasingly important research area to address concerns of LLMs in terms of privacy, copyright, etc. A conventional LLM unlearning task typically involves two goals: (1) The target LLM should forget the knowledge in the specified forget documents, and (2) it should retain the other knowledge that the LLM possesses, for which we assume access to a small number of retain documents. To achieve both goals, a mainstream class of LLM unlearning methods introduces an optimization fr","authors_text":"Gaowen Liu, Jiabao Ji, Ramana Rao Kompella, Shiyu Chang, Sijia Liu, Yang Zhang, Yujian Liu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-12T19:26:35Z","title":"Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.08607","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":"sha256:3eb500b0acc8936a52c0ce9ada8bcf222adec1557dab90731b8bc610380da28a","target":"record","created_at":"2026-07-05T08:31:19Z","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":"32ddc87d287cb64968e9f65e5f1197426f9254e13c04d44c14c428b35e497735","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-12T19:26:35Z","title_canon_sha256":"b4cab41d1878ab8da391aac6d12858f0fdd5c44b997d5f5e62dc22891cb15b9f"},"schema_version":"1.0","source":{"id":"2406.08607","kind":"arxiv","version":1}},"canonical_sha256":"07d3555b8cf0a1104adf0587d6a26f18edf60e2a9add6b85c45cdbb41311f1e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07d3555b8cf0a1104adf0587d6a26f18edf60e2a9add6b85c45cdbb41311f1e4","first_computed_at":"2026-07-05T08:31:19.976059Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:31:19.976059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jr1zKHJ5kv9b8B6uSB5ntnsnxdHpJ5+fXvedp02x4DZisKCgZUXQegjXVd9wPrxZXI1gIBMOK2pFSiloa8n7Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:31:19.976549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.08607","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3eb500b0acc8936a52c0ce9ada8bcf222adec1557dab90731b8bc610380da28a","sha256:30ef9efbf35d3623a5b22b0140a2821630061eb45f413ef03bbad12bc248c7d8"],"state_sha256":"356bf21cf6e53df3690d9feb90584ad7fc262424f6f7e163063ae11e737f1d75"}