{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HYHMAPFKKWUSF4CGEXJXBX274V","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":"9519833c4a8546da646dee6c8ba6c8205b29f481bb4076bd12ba327869dff5b1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T04:14:34Z","title_canon_sha256":"534ce725af3608a261708d0f313933e7d3b9a9ed289d2853fbc3c2b9e829a7ba"},"schema_version":"1.0","source":{"id":"2502.06820","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.06820","created_at":"2026-07-05T10:55:30Z"},{"alias_kind":"arxiv_version","alias_value":"2502.06820v2","created_at":"2026-07-05T10:55:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06820","created_at":"2026-07-05T10:55:30Z"},{"alias_kind":"pith_short_12","alias_value":"HYHMAPFKKWUS","created_at":"2026-07-05T10:55:30Z"},{"alias_kind":"pith_short_16","alias_value":"HYHMAPFKKWUSF4CG","created_at":"2026-07-05T10:55:30Z"},{"alias_kind":"pith_short_8","alias_value":"HYHMAPFK","created_at":"2026-07-05T10:55:30Z"}],"graph_snapshots":[{"event_id":"sha256:01fab472a81d2e4313a9df896508716e97e5a37f0c17c4cdf6654d630fefe4eb","target":"graph","created_at":"2026-07-05T10:55:30Z","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/2502.06820/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the hypothesis space. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain parameter-efficient fine-tuning method based on inverse Discrete Cosine Transform (iDCT) with selective locations of learnable components. We begin with a comprehensive theoretical comparison between frequency-domain and low-rank decompositions for fine-tuning pre-trained large model","authors_text":"Changliang Zou, Jingjing Li, Ke Lu, Liuhua Peng, Mingming Gong, Tingjin Chu, Yinjie Min, Zhekai Du","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T04:14:34Z","title":"LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06820","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":"sha256:0c24cab73a270ee9066b32f61d5e14e60e632991f699f357caf18a48a4f27ece","target":"record","created_at":"2026-07-05T10:55:30Z","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":"9519833c4a8546da646dee6c8ba6c8205b29f481bb4076bd12ba327869dff5b1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-05T04:14:34Z","title_canon_sha256":"534ce725af3608a261708d0f313933e7d3b9a9ed289d2853fbc3c2b9e829a7ba"},"schema_version":"1.0","source":{"id":"2502.06820","kind":"arxiv","version":2}},"canonical_sha256":"3e0ec03caa55a922f04625d370df5fe547306b7afc48ee4d1582fd4d505c7f20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e0ec03caa55a922f04625d370df5fe547306b7afc48ee4d1582fd4d505c7f20","first_computed_at":"2026-07-05T10:55:30.312106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:55:30.312106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yKjHzKCtTeRPFJan6ZnFTgexZb1UF7ynWa6DaZpe7d0F6eGpl5zPwP3NHK1wjwm/+PXw7hRdpbcdJBcFPPEjDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:55:30.312678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.06820","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c24cab73a270ee9066b32f61d5e14e60e632991f699f357caf18a48a4f27ece","sha256:01fab472a81d2e4313a9df896508716e97e5a37f0c17c4cdf6654d630fefe4eb"],"state_sha256":"72b0f692a97a8699ae9ab677b728e0aa323533c38d2c1b1d0d63103927a8329b"}