{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:25432O6XVOFBGNWJNCGN4X5GVT","short_pith_number":"pith:25432O6X","schema_version":"1.0","canonical_sha256":"d779bd3bd7ab8a1336c9688cde5fa6acecee271749b2192db1b7295a20c9f6dd","source":{"kind":"arxiv","id":"2408.03290","version":1},"attestation_state":"computed","paper":{"title":"SARA: Singular-Value Based Adaptive Low-Rank Adaption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Jihao Gu, Ping Gong, Shuai Chen, Yibo Zhang, Zelin Wang","submitted_at":"2024-08-06T16:39:42Z","abstract_excerpt":"With the increasing number of parameters in large pre-trained models, LoRA as a parameter-efficient fine-tuning(PEFT) method is widely used for not adding inference overhead. The LoRA method assumes that weight changes during fine-tuning can be approximated by low-rank matrices. However, the rank values need to be manually verified to match different downstream tasks, and they cannot accommodate the varying importance of different layers in the model. In this work, we first analyze the relationship between the performance of different layers and their ranks using SVD. Based on this, we design "},"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":"2408.03290","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-06T16:39:42Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"3ae9f99467846c913f2f80bebbc9812aea8a5097b98cbd5fcc70ea99b93ef4ce","abstract_canon_sha256":"083014ceef77c488aed675663f7e73a666b0a1ef6c5b2ed1ccdb5e4ebee10d5d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:48.555926Z","signature_b64":"xuaJ+ZabyBB/HeDdGDf6NjU+LpWx86dyAqVirXo6VOjrumq4pB8OjimfJ9MA5ASMuhXBycQnhQy9e7PbYykVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d779bd3bd7ab8a1336c9688cde5fa6acecee271749b2192db1b7295a20c9f6dd","last_reissued_at":"2026-07-05T08:52:48.555543Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:48.555543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SARA: Singular-Value Based Adaptive Low-Rank Adaption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Jihao Gu, Ping Gong, Shuai Chen, Yibo Zhang, Zelin Wang","submitted_at":"2024-08-06T16:39:42Z","abstract_excerpt":"With the increasing number of parameters in large pre-trained models, LoRA as a parameter-efficient fine-tuning(PEFT) method is widely used for not adding inference overhead. The LoRA method assumes that weight changes during fine-tuning can be approximated by low-rank matrices. However, the rank values need to be manually verified to match different downstream tasks, and they cannot accommodate the varying importance of different layers in the model. In this work, we first analyze the relationship between the performance of different layers and their ranks using SVD. Based on this, we design "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03290","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/2408.03290/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":"2408.03290","created_at":"2026-07-05T08:52:48.555599+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03290v1","created_at":"2026-07-05T08:52:48.555599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03290","created_at":"2026-07-05T08:52:48.555599+00:00"},{"alias_kind":"pith_short_12","alias_value":"25432O6XVOFB","created_at":"2026-07-05T08:52:48.555599+00:00"},{"alias_kind":"pith_short_16","alias_value":"25432O6XVOFBGNWJ","created_at":"2026-07-05T08:52:48.555599+00:00"},{"alias_kind":"pith_short_8","alias_value":"25432O6X","created_at":"2026-07-05T08:52:48.555599+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10009","citing_title":"Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT","json":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT.json","graph_json":"https://pith.science/api/pith-number/25432O6XVOFBGNWJNCGN4X5GVT/graph.json","events_json":"https://pith.science/api/pith-number/25432O6XVOFBGNWJNCGN4X5GVT/events.json","paper":"https://pith.science/paper/25432O6X"},"agent_actions":{"view_html":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT","download_json":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT.json","view_paper":"https://pith.science/paper/25432O6X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03290&json=true","fetch_graph":"https://pith.science/api/pith-number/25432O6XVOFBGNWJNCGN4X5GVT/graph.json","fetch_events":"https://pith.science/api/pith-number/25432O6XVOFBGNWJNCGN4X5GVT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT/action/storage_attestation","attest_author":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT/action/author_attestation","sign_citation":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT/action/citation_signature","submit_replication":"https://pith.science/pith/25432O6XVOFBGNWJNCGN4X5GVT/action/replication_record"}},"created_at":"2026-07-05T08:52:48.555599+00:00","updated_at":"2026-07-05T08:52:48.555599+00:00"}