{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WUKUV6V2TYZQUEQ4TRYUEYCDMJ","short_pith_number":"pith:WUKUV6V2","schema_version":"1.0","canonical_sha256":"b5154afaba9e330a121c9c71426043626ce4289906575332640e92f76f32627c","source":{"kind":"arxiv","id":"2404.19245","version":2},"attestation_state":"computed","paper":{"title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chengzhong Xu, Chunlin Tian, Li Li, Zhan Shi, Zhijiang Guo","submitted_at":"2024-04-30T04:01:09Z","abstract_excerpt":"Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fine-tuning, particularly in scenarios involving complex datasets. This issue becomes even more pronounced in complex domains, highlighting the need for improved PEFT approaches that can achieve better performance. Through a series of experiments, we have uncovered two critical insights that shed light on the training and parameter inefficiency of LoR"},"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":"2404.19245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-30T04:01:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"788b807322ea89e1ad7e5f7d275fdbfd980a855428aa0164cbe4fa7de1613c20","abstract_canon_sha256":"24773ec6cf50bb3714a9749998d4ab8fc403b12fff41a1c64af7764d12940d22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:22.995200Z","signature_b64":"MMhGOJMdAqX/bxZINJ5hcOfDleipkA5clEQFtzd9b2Z7gHFvhHHAJBAWdk9vDy2gxTi5pHW7LL7L6ZFUMBw3DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5154afaba9e330a121c9c71426043626ce4289906575332640e92f76f32627c","last_reissued_at":"2026-07-05T08:22:22.994702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:22.994702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chengzhong Xu, Chunlin Tian, Li Li, Zhan Shi, Zhijiang Guo","submitted_at":"2024-04-30T04:01:09Z","abstract_excerpt":"Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fine-tuning, particularly in scenarios involving complex datasets. This issue becomes even more pronounced in complex domains, highlighting the need for improved PEFT approaches that can achieve better performance. Through a series of experiments, we have uncovered two critical insights that shed light on the training and parameter inefficiency of LoR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.19245","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/2404.19245/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":"2404.19245","created_at":"2026-07-05T08:22:22.994770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.19245v2","created_at":"2026-07-05T08:22:22.994770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.19245","created_at":"2026-07-05T08:22:22.994770+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUKUV6V2TYZQ","created_at":"2026-07-05T08:22:22.994770+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUKUV6V2TYZQUEQ4","created_at":"2026-07-05T08:22:22.994770+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUKUV6V2","created_at":"2026-07-05T08:22:22.994770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.19926","citing_title":"Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06175","citing_title":"VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13368","citing_title":"TLoRA+: A Low-Rank Parameter-Efficient Fine-Tuning Method for Large Language Models","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ","json":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ.json","graph_json":"https://pith.science/api/pith-number/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/graph.json","events_json":"https://pith.science/api/pith-number/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/events.json","paper":"https://pith.science/paper/WUKUV6V2"},"agent_actions":{"view_html":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ","download_json":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ.json","view_paper":"https://pith.science/paper/WUKUV6V2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.19245&json=true","fetch_graph":"https://pith.science/api/pith-number/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/action/storage_attestation","attest_author":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/action/author_attestation","sign_citation":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/action/citation_signature","submit_replication":"https://pith.science/pith/WUKUV6V2TYZQUEQ4TRYUEYCDMJ/action/replication_record"}},"created_at":"2026-07-05T08:22:22.994770+00:00","updated_at":"2026-07-05T08:22:22.994770+00:00"}