{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DMO2TT4XKB74OF6F43UYJG4VLH","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":"727a81afded0e17b1a4a8b949024f2b2ff05ff902459e86cddf870ed071d9f15","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T09:32:58Z","title_canon_sha256":"8182837bd714d16215419967e5bd35835bd94717d35a9a1523def54362ccc680"},"schema_version":"1.0","source":{"id":"2506.07587","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07587","created_at":"2026-07-05T11:18:29Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07587v1","created_at":"2026-07-05T11:18:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07587","created_at":"2026-07-05T11:18:29Z"},{"alias_kind":"pith_short_12","alias_value":"DMO2TT4XKB74","created_at":"2026-07-05T11:18:29Z"},{"alias_kind":"pith_short_16","alias_value":"DMO2TT4XKB74OF6F","created_at":"2026-07-05T11:18:29Z"},{"alias_kind":"pith_short_8","alias_value":"DMO2TT4X","created_at":"2026-07-05T11:18:29Z"}],"graph_snapshots":[{"event_id":"sha256:87152ca4f6e1742aaeef78c62c6d0332b9b36320c996ea751f66c44bc16078f1","target":"graph","created_at":"2026-07-05T11:18:29Z","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/2506.07587/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Parameter Efficient Fine-Tuning (PEFT) methods have emerged as effective and promising approaches for fine-tuning pre-trained language models. Compared with Full parameter Fine-Tuning (FFT), PEFT achieved comparable task performance with a substantial reduction of trainable parameters, which largely saved the training and storage costs. However, using the PEFT method requires considering a vast design space, such as the type of PEFT modules and their insertion layers. Inadequate configurations can lead to sub-optimal results. Conventional solutions such as architectural search techniques, whil","authors_text":"Guanghui Zhu, Meikang Qiu, Shen Jiang, Tongzhou Yu, Yihua Huang, Zhuhao Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T09:32:58Z","title":"PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07587","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":"sha256:4f3c5d085bfadbb2ecf922ffc38fc55766cf0bfa57309a1ff90acd50d14f2642","target":"record","created_at":"2026-07-05T11:18:29Z","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":"727a81afded0e17b1a4a8b949024f2b2ff05ff902459e86cddf870ed071d9f15","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T09:32:58Z","title_canon_sha256":"8182837bd714d16215419967e5bd35835bd94717d35a9a1523def54362ccc680"},"schema_version":"1.0","source":{"id":"2506.07587","kind":"arxiv","version":1}},"canonical_sha256":"1b1da9cf97507fc717c5e6e9849b9559cdcb07c0ccb27c0c0dd5c0d5650a46ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b1da9cf97507fc717c5e6e9849b9559cdcb07c0ccb27c0c0dd5c0d5650a46ff","first_computed_at":"2026-07-05T11:18:29.873517Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:29.873517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XC55L7GUROtLISFfinwE5gzM+xjSX5Z1EPyCQSgIMR9lHi8W4wEKcpURakIhfzwN65FjtTIyqZYEbqKpbQNhBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:29.873964Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07587","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f3c5d085bfadbb2ecf922ffc38fc55766cf0bfa57309a1ff90acd50d14f2642","sha256:87152ca4f6e1742aaeef78c62c6d0332b9b36320c996ea751f66c44bc16078f1"],"state_sha256":"40bd4ff27518527b3060cd95339345736d9994b87094cd0f8edcfd0f0bb086bd"}