{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MD5N3MWVXFXZKLCBROOALSBGU7","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":"0654ec728500f48cf2fddac78a07ce667d78f9bebcac726b82840a6ffb7df545","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-02-04T19:36:20Z","title_canon_sha256":"20f9ce94e971f0f078a3aa46424b0869731e012450574ea3123810faf8d75eba"},"schema_version":"1.0","source":{"id":"2602.04998","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.04998","created_at":"2026-05-20T01:05:08Z"},{"alias_kind":"arxiv_version","alias_value":"2602.04998v2","created_at":"2026-05-20T01:05:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.04998","created_at":"2026-05-20T01:05:08Z"},{"alias_kind":"pith_short_12","alias_value":"MD5N3MWVXFXZ","created_at":"2026-05-20T01:05:08Z"},{"alias_kind":"pith_short_16","alias_value":"MD5N3MWVXFXZKLCB","created_at":"2026-05-20T01:05:08Z"},{"alias_kind":"pith_short_8","alias_value":"MD5N3MWV","created_at":"2026-05-20T01:05:08Z"}],"graph_snapshots":[{"event_id":"sha256:94e995a0ce8b5be770b2efeb0cb14383fade8f691a8c0c5f3dff65eec566c574","target":"graph","created_at":"2026-05-20T01:05:08Z","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/2602.04998/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies, architectural modifications, and optimization adjustments, reporting substantial improvements over vanilla LoRA. However, these gains are often demonstrated under fixed or narrowly tuned hyperparameter settings, despite the known sensitivity of neural networks to training configurations. In this work, we systematically re-evaluate nine representative LoRA variants alongside vanilla LoRA through","authors_text":"Ching-Yun Ko, Mi-Yen Yeh, Pin-Yu Chen, Yu-Ang Lee","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-02-04T19:36:20Z","title":"Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.04998","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":"sha256:3793d8fa6ffa48c6190cd369130b142c57527c2239ece96472311aaef9bd928f","target":"record","created_at":"2026-05-20T01:05:08Z","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":"0654ec728500f48cf2fddac78a07ce667d78f9bebcac726b82840a6ffb7df545","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-02-04T19:36:20Z","title_canon_sha256":"20f9ce94e971f0f078a3aa46424b0869731e012450574ea3123810faf8d75eba"},"schema_version":"1.0","source":{"id":"2602.04998","kind":"arxiv","version":2}},"canonical_sha256":"60faddb2d5b96f952c418b9c05c826a7d3d0bb0c8b153b210d1a32e6558d4200","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"60faddb2d5b96f952c418b9c05c826a7d3d0bb0c8b153b210d1a32e6558d4200","first_computed_at":"2026-05-20T01:05:08.193690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:08.193690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pqb0dek2ayXddrk2BvSbCAaqkGY/QlsumHa5JQGKDbqh5it0/PFY6XwaLIcHrr6yclUl4+Nd5b2fYRWgWa2fBQ==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:08.194532Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.04998","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3793d8fa6ffa48c6190cd369130b142c57527c2239ece96472311aaef9bd928f","sha256:94e995a0ce8b5be770b2efeb0cb14383fade8f691a8c0c5f3dff65eec566c574"],"state_sha256":"616b5ec75ebe54fe553a780c5b14a88bd893d66f418f1b332e7d7f05070a2761"}