{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2A3SG3QRTBYUZCCZXVD2WVESTC","short_pith_number":"pith:2A3SG3QR","schema_version":"1.0","canonical_sha256":"d037236e1198714c8859bd47ab549298803a4fb6094f0ef9583849f02bd8d2f9","source":{"kind":"arxiv","id":"2607.21612","version":1},"attestation_state":"computed","paper":{"title":"Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Hao Guo, Kevin Shabahang, Rivaan Patil, Simon Dennis","submitted_at":"2026-05-23T17:06:02Z","abstract_excerpt":"Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation. We show that for procedural knowledge--the ability to follow multi-step procedures with conditional branching through to terminal states--LoRA fails to match full fine-tuning at the ranks where it retains its efficiency advantage. In a systematic ablation (r = 16--128) on a procedural travel booking task (14 nodes), all LoRA configurations fail uniformly (task success <= 2.54 vs. 4.11 for full fine-tuning,"},"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":"2607.21612","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-23T17:06:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"efb3b9e5fe09adc2a625f4f09a1817f67abbde0eb93f74a20e422f8d01668897","abstract_canon_sha256":"331f64381046b1cabdaf581892862cce8d9aab24ef35d0d9b706bc499bf14fde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T00:20:11.065640Z","signature_b64":"8ya7NUAOPE/KlJi3JVwSzyjHCx6axttz65kKMeijdKuf/ZiJl2Fjt7jqgWrmu+mUwk05SmqU5fqF3pcXm9JJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d037236e1198714c8859bd47ab549298803a4fb6094f0ef9583849f02bd8d2f9","last_reissued_at":"2026-07-27T00:20:11.064803Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T00:20:11.064803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Procedural Knowledge Is Not Low-Rank: Why LoRA Fails to Internalize Multi-Step Procedures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Hao Guo, Kevin Shabahang, Rivaan Patil, Simon Dennis","submitted_at":"2026-05-23T17:06:02Z","abstract_excerpt":"Parameter-efficient fine-tuning methods like LoRA have become the default for adapting large language models, succeeding across instruction following, style transfer, and factual adaptation. We show that for procedural knowledge--the ability to follow multi-step procedures with conditional branching through to terminal states--LoRA fails to match full fine-tuning at the ranks where it retains its efficiency advantage. In a systematic ablation (r = 16--128) on a procedural travel booking task (14 nodes), all LoRA configurations fail uniformly (task success <= 2.54 vs. 4.11 for full fine-tuning,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.21612","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/2607.21612/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":"2607.21612","created_at":"2026-07-27T00:20:11.065230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.21612v1","created_at":"2026-07-27T00:20:11.065230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.21612","created_at":"2026-07-27T00:20:11.065230+00:00"},{"alias_kind":"pith_short_12","alias_value":"2A3SG3QRTBYU","created_at":"2026-07-27T00:20:11.065230+00:00"},{"alias_kind":"pith_short_16","alias_value":"2A3SG3QRTBYUZCCZ","created_at":"2026-07-27T00:20:11.065230+00:00"},{"alias_kind":"pith_short_8","alias_value":"2A3SG3QR","created_at":"2026-07-27T00:20:11.065230+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/2A3SG3QRTBYUZCCZXVD2WVESTC","json":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC.json","graph_json":"https://pith.science/api/pith-number/2A3SG3QRTBYUZCCZXVD2WVESTC/graph.json","events_json":"https://pith.science/api/pith-number/2A3SG3QRTBYUZCCZXVD2WVESTC/events.json","paper":"https://pith.science/paper/2A3SG3QR"},"agent_actions":{"view_html":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC","download_json":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC.json","view_paper":"https://pith.science/paper/2A3SG3QR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.21612&json=true","fetch_graph":"https://pith.science/api/pith-number/2A3SG3QRTBYUZCCZXVD2WVESTC/graph.json","fetch_events":"https://pith.science/api/pith-number/2A3SG3QRTBYUZCCZXVD2WVESTC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC/action/storage_attestation","attest_author":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC/action/author_attestation","sign_citation":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC/action/citation_signature","submit_replication":"https://pith.science/pith/2A3SG3QRTBYUZCCZXVD2WVESTC/action/replication_record"}},"created_at":"2026-07-27T00:20:11.065230+00:00","updated_at":"2026-07-27T00:20:11.065230+00:00"}