{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:LCUR4CEW63R6BCICR7OURKYM4V","short_pith_number":"pith:LCUR4CEW","canonical_record":{"source":{"id":"2605.19018","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T18:40:24Z","cross_cats_sorted":[],"title_canon_sha256":"b6afd391b1a3e73cde0ae50faf1e64540538987467ad8b40ac60382aee312c4b","abstract_canon_sha256":"da8a99251c625dce98229bd6b5b90acfacfc6a22ac08abcff6bd96b4cf554edc"},"schema_version":"1.0"},"canonical_sha256":"58a91e0896f6e3e089028fdd48ab0ce55627a2498d2f7864a8c900163584c55b","source":{"kind":"arxiv","id":"2605.19018","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19018","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19018v1","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19018","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"LCUR4CEW63R6","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"LCUR4CEW63R6BCIC","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"LCUR4CEW","created_at":"2026-05-20T01:04:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:LCUR4CEW63R6BCICR7OURKYM4V","target":"record","payload":{"canonical_record":{"source":{"id":"2605.19018","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T18:40:24Z","cross_cats_sorted":[],"title_canon_sha256":"b6afd391b1a3e73cde0ae50faf1e64540538987467ad8b40ac60382aee312c4b","abstract_canon_sha256":"da8a99251c625dce98229bd6b5b90acfacfc6a22ac08abcff6bd96b4cf554edc"},"schema_version":"1.0"},"canonical_sha256":"58a91e0896f6e3e089028fdd48ab0ce55627a2498d2f7864a8c900163584c55b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:04:42.668952Z","signature_b64":"L7DdYYvmvGN7A473LaoCNHcHOpAR3VPkq+ByayCQ4LI/B6Vr3EiLomAx7Ncib/D9YfIbSk2cxJ3gxULl4QH5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58a91e0896f6e3e089028fdd48ab0ce55627a2498d2f7864a8c900163584c55b","last_reissued_at":"2026-05-20T01:04:42.668088Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:04:42.668088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.19018","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AIcEWOTsdV5qC5zX4IFtMr9z27/pxHy20HToSnkp8xPByEpKEd57IOP5+5bNNzbUz0d/CCBz2Lus0K9LdZ/VDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:49:23.183493Z"},"content_sha256":"4fefce6a57775ad35cb31c5155a09f93da1b62e2b4c1e791fa7ced5c87312398","schema_version":"1.0","event_id":"sha256:4fefce6a57775ad35cb31c5155a09f93da1b62e2b4c1e791fa7ced5c87312398"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:LCUR4CEW63R6BCICR7OURKYM4V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Zindari, Rotem Mulayoff, Sebastian U. Stich","submitted_at":"2026-05-18T18:40:24Z","abstract_excerpt":"Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that reduces memory and computation costs while often achieving performance close to full fine-tuning. Despite its widespread use, the theoretical behavior of LoRA is not yet well understood. In this paper, we study LoRA in a simple linear regression setting and compare its excess risk with that of full fine-tuning. Our analysis identifies regimes in which LoRA achieves lower excess risk than full fine-tuning in both overdetermined and u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19018","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/2605.19018/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T01:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oat0DYI9im9+HTc9gJYrh/fpYzAFsv/mAdOdTeCsqO2tkQoF5zLgXPrCYVPFQIoudcq7tyTArJ2eTrcKKKovAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:49:23.184155Z"},"content_sha256":"486fc9e0258adb1dd767955a5ef4a0a98bfc670a2187917c02d5f132d2ae085d","schema_version":"1.0","event_id":"sha256:486fc9e0258adb1dd767955a5ef4a0a98bfc670a2187917c02d5f132d2ae085d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LCUR4CEW63R6BCICR7OURKYM4V/bundle.json","state_url":"https://pith.science/pith/LCUR4CEW63R6BCICR7OURKYM4V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LCUR4CEW63R6BCICR7OURKYM4V/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T11:49:23Z","links":{"resolver":"https://pith.science/pith/LCUR4CEW63R6BCICR7OURKYM4V","bundle":"https://pith.science/pith/LCUR4CEW63R6BCICR7OURKYM4V/bundle.json","state":"https://pith.science/pith/LCUR4CEW63R6BCICR7OURKYM4V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LCUR4CEW63R6BCICR7OURKYM4V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LCUR4CEW63R6BCICR7OURKYM4V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"da8a99251c625dce98229bd6b5b90acfacfc6a22ac08abcff6bd96b4cf554edc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T18:40:24Z","title_canon_sha256":"b6afd391b1a3e73cde0ae50faf1e64540538987467ad8b40ac60382aee312c4b"},"schema_version":"1.0","source":{"id":"2605.19018","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19018","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19018v1","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19018","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"LCUR4CEW63R6","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"LCUR4CEW63R6BCIC","created_at":"2026-05-20T01:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"LCUR4CEW","created_at":"2026-05-20T01:04:42Z"}],"graph_snapshots":[{"event_id":"sha256:486fc9e0258adb1dd767955a5ef4a0a98bfc670a2187917c02d5f132d2ae085d","target":"graph","created_at":"2026-05-20T01:04:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2605.19018/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that reduces memory and computation costs while often achieving performance close to full fine-tuning. Despite its widespread use, the theoretical behavior of LoRA is not yet well understood. In this paper, we study LoRA in a simple linear regression setting and compare its excess risk with that of full fine-tuning. Our analysis identifies regimes in which LoRA achieves lower excess risk than full fine-tuning in both overdetermined and u","authors_text":"Ali Zindari, Rotem Mulayoff, Sebastian U. Stich","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T18:40:24Z","title":"LoRA vs. Full Fine-Tuning: A Theoretical Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19018","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4fefce6a57775ad35cb31c5155a09f93da1b62e2b4c1e791fa7ced5c87312398","target":"record","created_at":"2026-05-20T01:04:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"da8a99251c625dce98229bd6b5b90acfacfc6a22ac08abcff6bd96b4cf554edc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T18:40:24Z","title_canon_sha256":"b6afd391b1a3e73cde0ae50faf1e64540538987467ad8b40ac60382aee312c4b"},"schema_version":"1.0","source":{"id":"2605.19018","kind":"arxiv","version":1}},"canonical_sha256":"58a91e0896f6e3e089028fdd48ab0ce55627a2498d2f7864a8c900163584c55b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58a91e0896f6e3e089028fdd48ab0ce55627a2498d2f7864a8c900163584c55b","first_computed_at":"2026-05-20T01:04:42.668088Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:04:42.668088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L7DdYYvmvGN7A473LaoCNHcHOpAR3VPkq+ByayCQ4LI/B6Vr3EiLomAx7Ncib/D9YfIbSk2cxJ3gxULl4QH5AA==","signature_status":"signed_v1","signed_at":"2026-05-20T01:04:42.668952Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.19018","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fefce6a57775ad35cb31c5155a09f93da1b62e2b4c1e791fa7ced5c87312398","sha256:486fc9e0258adb1dd767955a5ef4a0a98bfc670a2187917c02d5f132d2ae085d"],"state_sha256":"bdece47cc8ba84f6547f33ac8cc577c25a84a96a432fec469fef6f8402293d50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e8JpIkTBxq+ZHaf1vfBnTjJjvsoNZsUwyRF6qKWX6tI9Ehvft7AWWkpcHKYh1GGfsmyGbvOujMjGXEgzg8t0Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:49:23.187800Z","bundle_sha256":"0b5936bcf05f1ff9e72615ac7364d66e14905337c401b0dff1d6315d2c2fb5ce"}}