{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:K7H4TONGXZSBCIPVEWXM6RUGE6","short_pith_number":"pith:K7H4TONG","canonical_record":{"source":{"id":"2407.05000","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-06T08:37:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"33feb2546a18123593eceda9e016fda20bebc02d77269d00bc6dff7a3308c8a1","abstract_canon_sha256":"db2f7819155cee6899819ac8570638ac6cd6e4cab714274905bb71dd5608af47"},"schema_version":"1.0"},"canonical_sha256":"57cfc9b9a6be641121f525aecf468627a92d0885e9c56fcca6ad665ecafa21c0","source":{"kind":"arxiv","id":"2407.05000","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.05000","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.05000v2","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05000","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_12","alias_value":"K7H4TONGXZSB","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_16","alias_value":"K7H4TONGXZSBCIPV","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_8","alias_value":"K7H4TONG","created_at":"2026-07-05T08:44:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:K7H4TONGXZSBCIPVEWXM6RUGE6","target":"record","payload":{"canonical_record":{"source":{"id":"2407.05000","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-06T08:37:21Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"33feb2546a18123593eceda9e016fda20bebc02d77269d00bc6dff7a3308c8a1","abstract_canon_sha256":"db2f7819155cee6899819ac8570638ac6cd6e4cab714274905bb71dd5608af47"},"schema_version":"1.0"},"canonical_sha256":"57cfc9b9a6be641121f525aecf468627a92d0885e9c56fcca6ad665ecafa21c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:15.440791Z","signature_b64":"oipxliJuxonua4R+9fn+8Z+mIlNkP9HYk2MEbk/SWbEUR4jh/g003jMDD0U3vuaprx7TW5i/LSNDQCrbOS12Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57cfc9b9a6be641121f525aecf468627a92d0885e9c56fcca6ad665ecafa21c0","last_reissued_at":"2026-07-05T08:44:15.440354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:15.440354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.05000","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:44:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"492WW8bWFTh0/poQcIaCBodSVPmjwzq5jitkaz7G3gHbfMZJF8H5jEvdbH31B5hH0vkLEKk1YxBG7IpJyUQACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:47:24.610102Z"},"content_sha256":"5b74649c17d3900740e6275f58cc56bfa218778bec980c0aa87f2e475e5be37f","schema_version":"1.0","event_id":"sha256:5b74649c17d3900740e6275f58cc56bfa218778bec980c0aa87f2e475e5be37f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:K7H4TONGXZSBCIPVEWXM6RUGE6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Jian Li, Linxi Yu, Shaowen Wang","submitted_at":"2024-07-06T08:37:21Z","abstract_excerpt":"Fine-tuning large-scale pretrained models is prohibitively expensive in terms of computational and memory costs. LoRA, as one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, offers a cost-effective alternative by fine-tuning an auxiliary low-rank model that has significantly fewer parameters. Although LoRA reduces the computational and memory requirements significantly at each iteration, extensive empirical evidence indicates that it converges at a considerably slower rate compared to full fine-tuning, ultimately leading to increased overall compute and often worse test per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05000","kind":"arxiv","version":2},"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/2407.05000/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":"2026-07-05T08:44:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CHmzxUAIw4W5aOQ5eKF22Nwf/4CVcLUxy00/4yIph/CIzO7wjjjxcxyuWI7fSjgSjIc140A0Auyu/J2fkiSrCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:47:24.610790Z"},"content_sha256":"2c8a551e26c1ec4e6557d21c8f87ef9df58122edcdaf94ec5c9c363bb16614ad","schema_version":"1.0","event_id":"sha256:2c8a551e26c1ec4e6557d21c8f87ef9df58122edcdaf94ec5c9c363bb16614ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/bundle.json","state_url":"https://pith.science/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/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":"2026-08-07T19:47:24Z","links":{"resolver":"https://pith.science/pith/K7H4TONGXZSBCIPVEWXM6RUGE6","bundle":"https://pith.science/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/bundle.json","state":"https://pith.science/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K7H4TONGXZSBCIPVEWXM6RUGE6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:K7H4TONGXZSBCIPVEWXM6RUGE6","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":"db2f7819155cee6899819ac8570638ac6cd6e4cab714274905bb71dd5608af47","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-06T08:37:21Z","title_canon_sha256":"33feb2546a18123593eceda9e016fda20bebc02d77269d00bc6dff7a3308c8a1"},"schema_version":"1.0","source":{"id":"2407.05000","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.05000","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"arxiv_version","alias_value":"2407.05000v2","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05000","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_12","alias_value":"K7H4TONGXZSB","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_16","alias_value":"K7H4TONGXZSBCIPV","created_at":"2026-07-05T08:44:15Z"},{"alias_kind":"pith_short_8","alias_value":"K7H4TONG","created_at":"2026-07-05T08:44:15Z"}],"graph_snapshots":[{"event_id":"sha256:2c8a551e26c1ec4e6557d21c8f87ef9df58122edcdaf94ec5c9c363bb16614ad","target":"graph","created_at":"2026-07-05T08:44:15Z","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/2407.05000/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large-scale pretrained models is prohibitively expensive in terms of computational and memory costs. LoRA, as one of the most popular Parameter-Efficient Fine-Tuning (PEFT) methods, offers a cost-effective alternative by fine-tuning an auxiliary low-rank model that has significantly fewer parameters. Although LoRA reduces the computational and memory requirements significantly at each iteration, extensive empirical evidence indicates that it converges at a considerably slower rate compared to full fine-tuning, ultimately leading to increased overall compute and often worse test per","authors_text":"Jian Li, Linxi Yu, Shaowen Wang","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-06T08:37:21Z","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05000","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":"sha256:5b74649c17d3900740e6275f58cc56bfa218778bec980c0aa87f2e475e5be37f","target":"record","created_at":"2026-07-05T08:44:15Z","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":"db2f7819155cee6899819ac8570638ac6cd6e4cab714274905bb71dd5608af47","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-06T08:37:21Z","title_canon_sha256":"33feb2546a18123593eceda9e016fda20bebc02d77269d00bc6dff7a3308c8a1"},"schema_version":"1.0","source":{"id":"2407.05000","kind":"arxiv","version":2}},"canonical_sha256":"57cfc9b9a6be641121f525aecf468627a92d0885e9c56fcca6ad665ecafa21c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57cfc9b9a6be641121f525aecf468627a92d0885e9c56fcca6ad665ecafa21c0","first_computed_at":"2026-07-05T08:44:15.440354Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:15.440354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oipxliJuxonua4R+9fn+8Z+mIlNkP9HYk2MEbk/SWbEUR4jh/g003jMDD0U3vuaprx7TW5i/LSNDQCrbOS12Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:15.440791Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.05000","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b74649c17d3900740e6275f58cc56bfa218778bec980c0aa87f2e475e5be37f","sha256:2c8a551e26c1ec4e6557d21c8f87ef9df58122edcdaf94ec5c9c363bb16614ad"],"state_sha256":"7af40ef764556bbec1264ecda4c6babedb7440d80081811b5630a775b66fd65f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rz4iPIoRInQg1G7l1n529Wn7O9ebzvkioKnVD8WoDBUobsQmiCJ6kZLu/LEoHBMhwuyZBxuUAghPFM7HNcqyAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:47:24.615541Z","bundle_sha256":"613438e9b109d43d30d7223644103ec26ed8cb56f9b93b1d653528a66ce5d5ca"}}