{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GV2PW525QTBPMSQZYH6TNJEM7T","short_pith_number":"pith:GV2PW525","schema_version":"1.0","canonical_sha256":"3574fb775d84c2f64a19c1fd36a48cfcf863bf4a93a09a99259e4bb938235dbb","source":{"kind":"arxiv","id":"2506.16456","version":1},"attestation_state":"computed","paper":{"title":"Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chao-Han Huck Yang, Chen-Yu Liu, Jun Qi, Min-Hsiu Hsieh, Sabato Marco Siniscalchi","submitted_at":"2025-06-19T16:46:23Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matrices, which inherently limits its expressivity and generalization capabilities. While classical tensor-train (TT) decomposition can be separately employed on individual LoRA matrices, this work demonstrates that the classical TT-based approach neither significantly improves parameter efficiency nor achieves substantial performance gains. This paper proposes TensorGuide, a novel tensor-train-guided adaptation framework "},"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":"2506.16456","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-19T16:46:23Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"61197f9241c0f7781e22ea27966bcc062f86bbab4ed6d2fbdc588e20c33086c5","abstract_canon_sha256":"2a2c14f785c2e77c07b8b3b61753538d7853109ce052da7f79985a7aded0b836"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:36.771715Z","signature_b64":"DuTBIcqs0c55tO5uRBM/fI9FW4PsjyKLPw51kI83L7yLYynuq+zXxwNrnrl7BI6nJSu1xN74TdiRXdRlI02ZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3574fb775d84c2f64a19c1fd36a48cfcf863bf4a93a09a99259e4bb938235dbb","last_reissued_at":"2026-07-05T11:24:36.771214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:36.771214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chao-Han Huck Yang, Chen-Yu Liu, Jun Qi, Min-Hsiu Hsieh, Sabato Marco Siniscalchi","submitted_at":"2025-06-19T16:46:23Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matrices, which inherently limits its expressivity and generalization capabilities. While classical tensor-train (TT) decomposition can be separately employed on individual LoRA matrices, this work demonstrates that the classical TT-based approach neither significantly improves parameter efficiency nor achieves substantial performance gains. This paper proposes TensorGuide, a novel tensor-train-guided adaptation framework "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16456","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/2506.16456/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":"2506.16456","created_at":"2026-07-05T11:24:36.771269+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.16456v1","created_at":"2026-07-05T11:24:36.771269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16456","created_at":"2026-07-05T11:24:36.771269+00:00"},{"alias_kind":"pith_short_12","alias_value":"GV2PW525QTBP","created_at":"2026-07-05T11:24:36.771269+00:00"},{"alias_kind":"pith_short_16","alias_value":"GV2PW525QTBPMSQZ","created_at":"2026-07-05T11:24:36.771269+00:00"},{"alias_kind":"pith_short_8","alias_value":"GV2PW525","created_at":"2026-07-05T11:24:36.771269+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T","json":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T.json","graph_json":"https://pith.science/api/pith-number/GV2PW525QTBPMSQZYH6TNJEM7T/graph.json","events_json":"https://pith.science/api/pith-number/GV2PW525QTBPMSQZYH6TNJEM7T/events.json","paper":"https://pith.science/paper/GV2PW525"},"agent_actions":{"view_html":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T","download_json":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T.json","view_paper":"https://pith.science/paper/GV2PW525","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.16456&json=true","fetch_graph":"https://pith.science/api/pith-number/GV2PW525QTBPMSQZYH6TNJEM7T/graph.json","fetch_events":"https://pith.science/api/pith-number/GV2PW525QTBPMSQZYH6TNJEM7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T/action/storage_attestation","attest_author":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T/action/author_attestation","sign_citation":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T/action/citation_signature","submit_replication":"https://pith.science/pith/GV2PW525QTBPMSQZYH6TNJEM7T/action/replication_record"}},"created_at":"2026-07-05T11:24:36.771269+00:00","updated_at":"2026-07-05T11:24:36.771269+00:00"}