{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TJWZA67J4VR4NYB7ZMBDF3YSVR","short_pith_number":"pith:TJWZA67J","schema_version":"1.0","canonical_sha256":"9a6d907be9e563c6e03fcb0232ef12ac474b85ac55042b568927c866208e4c57","source":{"kind":"arxiv","id":"2503.23360","version":1},"attestation_state":"computed","paper":{"title":"Not All LoRA Parameters Are Essential: Insights on Inference Necessity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ci-Jun Gao, Derek F. Wong, Feng Wan, Guanhua Chen, Lidia S. Chao, Yutong Yao","submitted_at":"2025-03-30T08:33:04Z","abstract_excerpt":"Current research on LoRA primarily focuses on minimizing the number of fine-tuned parameters or optimizing its architecture. However, the necessity of all fine-tuned LoRA layers during inference remains underexplored. In this paper, we investigate the contribution of each LoRA layer to the model's ability to predict the ground truth and hypothesize that lower-layer LoRA modules play a more critical role in model reasoning and understanding. To address this, we propose a simple yet effective method to enhance the performance of large language models (LLMs) fine-tuned with LoRA. Specifically, we"},"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":"2503.23360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-30T08:33:04Z","cross_cats_sorted":[],"title_canon_sha256":"464f0ea616517a359fec5f927d06709e72c9f59c6608b93a0a979507e2a0b948","abstract_canon_sha256":"4aaa02045598cc75ddfd4577bb4c1819779c271b74047f8dac6b3312f8a83dd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:41:35.032342Z","signature_b64":"4WpdXKG5VKLhe2imXwUTag7VBVijIUUP9GEVujHRGeUQI9DPERcUnaBbMT6eP+dHyPNce0RKCTBhs19pBb6/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a6d907be9e563c6e03fcb0232ef12ac474b85ac55042b568927c866208e4c57","last_reissued_at":"2026-07-05T10:41:35.031828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:41:35.031828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Not All LoRA Parameters Are Essential: Insights on Inference Necessity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ci-Jun Gao, Derek F. Wong, Feng Wan, Guanhua Chen, Lidia S. Chao, Yutong Yao","submitted_at":"2025-03-30T08:33:04Z","abstract_excerpt":"Current research on LoRA primarily focuses on minimizing the number of fine-tuned parameters or optimizing its architecture. However, the necessity of all fine-tuned LoRA layers during inference remains underexplored. In this paper, we investigate the contribution of each LoRA layer to the model's ability to predict the ground truth and hypothesize that lower-layer LoRA modules play a more critical role in model reasoning and understanding. To address this, we propose a simple yet effective method to enhance the performance of large language models (LLMs) fine-tuned with LoRA. Specifically, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.23360","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/2503.23360/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":"2503.23360","created_at":"2026-07-05T10:41:35.031890+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.23360v1","created_at":"2026-07-05T10:41:35.031890+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.23360","created_at":"2026-07-05T10:41:35.031890+00:00"},{"alias_kind":"pith_short_12","alias_value":"TJWZA67J4VR4","created_at":"2026-07-05T10:41:35.031890+00:00"},{"alias_kind":"pith_short_16","alias_value":"TJWZA67J4VR4NYB7","created_at":"2026-07-05T10:41:35.031890+00:00"},{"alias_kind":"pith_short_8","alias_value":"TJWZA67J","created_at":"2026-07-05T10:41:35.031890+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14928","citing_title":"Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR","json":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR.json","graph_json":"https://pith.science/api/pith-number/TJWZA67J4VR4NYB7ZMBDF3YSVR/graph.json","events_json":"https://pith.science/api/pith-number/TJWZA67J4VR4NYB7ZMBDF3YSVR/events.json","paper":"https://pith.science/paper/TJWZA67J"},"agent_actions":{"view_html":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR","download_json":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR.json","view_paper":"https://pith.science/paper/TJWZA67J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.23360&json=true","fetch_graph":"https://pith.science/api/pith-number/TJWZA67J4VR4NYB7ZMBDF3YSVR/graph.json","fetch_events":"https://pith.science/api/pith-number/TJWZA67J4VR4NYB7ZMBDF3YSVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR/action/storage_attestation","attest_author":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR/action/author_attestation","sign_citation":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR/action/citation_signature","submit_replication":"https://pith.science/pith/TJWZA67J4VR4NYB7ZMBDF3YSVR/action/replication_record"}},"created_at":"2026-07-05T10:41:35.031890+00:00","updated_at":"2026-07-05T10:41:35.031890+00:00"}