{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7LNH5C6J2BAZSKK7MNVS7JIP24","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":"c113fcf4151dbde3b6e244ffeb10671273a97d30efadc77f2f05fcd4281b22df","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-12T09:32:25Z","title_canon_sha256":"675b0c313c44a7e3928e9ae6eca97817230d8bee7d57502864da04dc5d492af4"},"schema_version":"1.0","source":{"id":"2403.07440","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.07440","created_at":"2026-07-05T08:02:25Z"},{"alias_kind":"arxiv_version","alias_value":"2403.07440v3","created_at":"2026-07-05T08:02:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07440","created_at":"2026-07-05T08:02:25Z"},{"alias_kind":"pith_short_12","alias_value":"7LNH5C6J2BAZ","created_at":"2026-07-05T08:02:25Z"},{"alias_kind":"pith_short_16","alias_value":"7LNH5C6J2BAZSKK7","created_at":"2026-07-05T08:02:25Z"},{"alias_kind":"pith_short_8","alias_value":"7LNH5C6J","created_at":"2026-07-05T08:02:25Z"}],"graph_snapshots":[{"event_id":"sha256:ea51dd089759429f1e8fefe0eb919af58b3df292f259c52131ddce8059bb94c0","target":"graph","created_at":"2026-07-05T08:02:25Z","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/2403.07440/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning techniques based on Large Pretrained Language Models (LPLMs) have been proven to significantly enhance model performance on a variety of downstream tasks and effectively control the output behaviors of LPLMs. Recent studies have proposed numerous methods for fine-tuning a small number of parameters based on open-source LPLMs, reducing the demand for computational and storage resources. Among these, reparameterization fine-tuning methods represented by LoRA (Low-Rank Adaptation) have gained popularity. We find that although these methods perform well in many aspects, there is still ","authors_text":"Yang Li, Yao Liang, Yi Zeng, Yuwei Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-12T09:32:25Z","title":"Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07440","kind":"arxiv","version":3},"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":"sha256:1802a65d4581fc948c7bb6fd679a19ed5c2ed55dd65a1a112926e08d113994e7","target":"record","created_at":"2026-07-05T08:02:25Z","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":"c113fcf4151dbde3b6e244ffeb10671273a97d30efadc77f2f05fcd4281b22df","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-12T09:32:25Z","title_canon_sha256":"675b0c313c44a7e3928e9ae6eca97817230d8bee7d57502864da04dc5d492af4"},"schema_version":"1.0","source":{"id":"2403.07440","kind":"arxiv","version":3}},"canonical_sha256":"fada7e8bc9d04199295f636b2fa50fd71b586d3de88fb1852f956dea2db02fea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fada7e8bc9d04199295f636b2fa50fd71b586d3de88fb1852f956dea2db02fea","first_computed_at":"2026-07-05T08:02:25.104106Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:25.104106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PsVYrHNjgxBJh2OUa84sefyAICUN7JnRwbCxF7XxHhh9IztLUF6xd9LkxW/l6arLBJ1qeypq3dnwDlwrhR3uBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:25.104611Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.07440","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1802a65d4581fc948c7bb6fd679a19ed5c2ed55dd65a1a112926e08d113994e7","sha256:ea51dd089759429f1e8fefe0eb919af58b3df292f259c52131ddce8059bb94c0"],"state_sha256":"d2f764075a66577296887f1beb5a65592625c5da2fc4f6d210066bf132e118e4"}