{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:AC6QXDGZIAPLO3LLPN75DCVQYQ","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":"f94feecabd754d7efad182f5622f4303ef4e27d05b61b238247221a813b6b04f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-14T06:29:22Z","title_canon_sha256":"bbf8ceac32983efc8a78d9aee4d5adb3446b546f60e0d8ed60981b03143cb887"},"schema_version":"1.0","source":{"id":"2210.07558","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.07558","created_at":"2026-07-05T06:02:30Z"},{"alias_kind":"arxiv_version","alias_value":"2210.07558v2","created_at":"2026-07-05T06:02:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07558","created_at":"2026-07-05T06:02:30Z"},{"alias_kind":"pith_short_12","alias_value":"AC6QXDGZIAPL","created_at":"2026-07-05T06:02:30Z"},{"alias_kind":"pith_short_16","alias_value":"AC6QXDGZIAPLO3LL","created_at":"2026-07-05T06:02:30Z"},{"alias_kind":"pith_short_8","alias_value":"AC6QXDGZ","created_at":"2026-07-05T06:02:30Z"}],"graph_snapshots":[{"event_id":"sha256:7a1ef327caf20df38dbc9a6676e74e82f896ce9ee6a82f47245c22fb5a3964a6","target":"graph","created_at":"2026-07-05T06:02:30Z","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/2210.07558/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the ever-growing size of pretrained models (PMs), fine-tuning them has become more expensive and resource-hungry. As a remedy, low-rank adapters (LoRA) keep the main pretrained weights of the model frozen and just introduce some learnable truncated SVD modules (so-called LoRA blocks) to the model. While LoRA blocks are parameter-efficient, they suffer from two major problems: first, the size of these blocks is fixed and cannot be modified after training (for example, if we need to change the rank of LoRA blocks, then we need to re-train them from scratch); second, optimizing their rank re","authors_text":"Ali Ghodsi, Ivan Kobyzev, Mehdi Rezagholizadeh, Mojtaba Valipour","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-14T06:29:22Z","title":"DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07558","kind":"arxiv","version":2},"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:b80a35a7504d5fefcc157b3220bc214a0edc4cc8cedec7491e7dc4a832185c5b","target":"record","created_at":"2026-07-05T06:02:30Z","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":"f94feecabd754d7efad182f5622f4303ef4e27d05b61b238247221a813b6b04f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-14T06:29:22Z","title_canon_sha256":"bbf8ceac32983efc8a78d9aee4d5adb3446b546f60e0d8ed60981b03143cb887"},"schema_version":"1.0","source":{"id":"2210.07558","kind":"arxiv","version":2}},"canonical_sha256":"00bd0b8cd9401eb76d6b7b7fd18ab0c41367bcdd94121c430d1741d3e2d18e18","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00bd0b8cd9401eb76d6b7b7fd18ab0c41367bcdd94121c430d1741d3e2d18e18","first_computed_at":"2026-07-05T06:02:30.172751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:02:30.172751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KbHMx49JLbUvs3hVuG++D3VzHPVwhVdTp0DRe2qKS6WZ+/GAWth3QZQg1Y12ff/M6O733zjXhE/VHy5HziuwCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:02:30.173235Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.07558","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b80a35a7504d5fefcc157b3220bc214a0edc4cc8cedec7491e7dc4a832185c5b","sha256:7a1ef327caf20df38dbc9a6676e74e82f896ce9ee6a82f47245c22fb5a3964a6"],"state_sha256":"f6a84929dcd13cb64b530be57a9d48e02b160e34ccc5379fac37c2ed06969cb1"}