{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:5BB62S33X2TPPHU4EK37DDT3JT","short_pith_number":"pith:5BB62S33","canonical_record":{"source":{"id":"2311.06243","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-10T18:59:54Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"28c321a11273cef619289f03267b4f9712cb544621f9075dd4a06aa0f4dab48e","abstract_canon_sha256":"1c360341cda0eb4d2270b6c70afb3d95ac64503ecbd5dec86d021666ffe7b811"},"schema_version":"1.0"},"canonical_sha256":"e843ed4b7bbea6f79e9c22b7f18e7b4cf4afb5dc0d6c5b5e4541dff648f8d972","source":{"kind":"arxiv","id":"2311.06243","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06243","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06243v2","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06243","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_12","alias_value":"5BB62S33X2TP","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_16","alias_value":"5BB62S33X2TPPHU4","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_8","alias_value":"5BB62S33","created_at":"2026-07-05T08:12:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:5BB62S33X2TPPHU4EK37DDT3JT","target":"record","payload":{"canonical_record":{"source":{"id":"2311.06243","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-10T18:59:54Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"28c321a11273cef619289f03267b4f9712cb544621f9075dd4a06aa0f4dab48e","abstract_canon_sha256":"1c360341cda0eb4d2270b6c70afb3d95ac64503ecbd5dec86d021666ffe7b811"},"schema_version":"1.0"},"canonical_sha256":"e843ed4b7bbea6f79e9c22b7f18e7b4cf4afb5dc0d6c5b5e4541dff648f8d972","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:46.042790Z","signature_b64":"o8adv9PYKSDqg5oAwxRoIvLIyZm8DAtdrvP2BgSb1SsRzRG8kEXd5t2NFlf3DwVAd/BNhaXicvrnbbvzXRAVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e843ed4b7bbea6f79e9c22b7f18e7b4cf4afb5dc0d6c5b5e4541dff648f8d972","last_reissued_at":"2026-07-05T08:12:46.042362Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:46.042362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.06243","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":"2026-07-05T08:12:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q8Mt0sQ+YFHfGbBPKXNmgGaCW1gYBYT7p0rDCd+pv205pEtf0/CW40d8ZF9IUvyGE4q1bzarjC0xdOeuJPjgAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:36:03.631121Z"},"content_sha256":"529f0ef138fa66a96c95e706bb2618d6b084d12120cdd25a02b8143ef2434563","schema_version":"1.0","event_id":"sha256:529f0ef138fa66a96c95e706bb2618d6b084d12120cdd25a02b8143ef2434563"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:5BB62S33X2TPPHU4EK37DDT3JT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Adrian Weller, Bernhard Sch\\\"olkopf, Haiwen Feng, Juyeon Heo, Longhui Yu, Michael J. Black, Songyou Peng, Weiyang Liu, Yandong Wen, Yao Feng, Yuliang Xiu, Yuxuan Xue, Zeju Qiu, Zhen Liu","submitted_at":"2023-11-10T18:59:54Z","abstract_excerpt":"Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06243","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/2311.06243/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":"2026-07-05T08:12:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UGIWAfoNg/LiWgAXOtdKj0D5c/nWb1sPAFKcr0PjF75oY6v+SChS1MSrDbY5B3lG2zMx+PEpQtefv983CwJACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:36:03.631671Z"},"content_sha256":"f4a4e0ab2c58ceaad1fde795e5e4796a08072068fd429f93864facc01613e855","schema_version":"1.0","event_id":"sha256:f4a4e0ab2c58ceaad1fde795e5e4796a08072068fd429f93864facc01613e855"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5BB62S33X2TPPHU4EK37DDT3JT/bundle.json","state_url":"https://pith.science/pith/5BB62S33X2TPPHU4EK37DDT3JT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5BB62S33X2TPPHU4EK37DDT3JT/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":"2026-08-05T15:36:03Z","links":{"resolver":"https://pith.science/pith/5BB62S33X2TPPHU4EK37DDT3JT","bundle":"https://pith.science/pith/5BB62S33X2TPPHU4EK37DDT3JT/bundle.json","state":"https://pith.science/pith/5BB62S33X2TPPHU4EK37DDT3JT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5BB62S33X2TPPHU4EK37DDT3JT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5BB62S33X2TPPHU4EK37DDT3JT","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":"1c360341cda0eb4d2270b6c70afb3d95ac64503ecbd5dec86d021666ffe7b811","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-10T18:59:54Z","title_canon_sha256":"28c321a11273cef619289f03267b4f9712cb544621f9075dd4a06aa0f4dab48e"},"schema_version":"1.0","source":{"id":"2311.06243","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06243","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06243v2","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06243","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_12","alias_value":"5BB62S33X2TP","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_16","alias_value":"5BB62S33X2TPPHU4","created_at":"2026-07-05T08:12:46Z"},{"alias_kind":"pith_short_8","alias_value":"5BB62S33","created_at":"2026-07-05T08:12:46Z"}],"graph_snapshots":[{"event_id":"sha256:f4a4e0ab2c58ceaad1fde795e5e4796a08072068fd429f93864facc01613e855","target":"graph","created_at":"2026-07-05T08:12:46Z","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/2311.06243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few","authors_text":"Adrian Weller, Bernhard Sch\\\"olkopf, Haiwen Feng, Juyeon Heo, Longhui Yu, Michael J. Black, Songyou Peng, Weiyang Liu, Yandong Wen, Yao Feng, Yuliang Xiu, Yuxuan Xue, Zeju Qiu, Zhen Liu","cross_cats":["cs.AI","cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-10T18:59:54Z","title":"Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06243","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":"sha256:529f0ef138fa66a96c95e706bb2618d6b084d12120cdd25a02b8143ef2434563","target":"record","created_at":"2026-07-05T08:12:46Z","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":"1c360341cda0eb4d2270b6c70afb3d95ac64503ecbd5dec86d021666ffe7b811","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-10T18:59:54Z","title_canon_sha256":"28c321a11273cef619289f03267b4f9712cb544621f9075dd4a06aa0f4dab48e"},"schema_version":"1.0","source":{"id":"2311.06243","kind":"arxiv","version":2}},"canonical_sha256":"e843ed4b7bbea6f79e9c22b7f18e7b4cf4afb5dc0d6c5b5e4541dff648f8d972","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e843ed4b7bbea6f79e9c22b7f18e7b4cf4afb5dc0d6c5b5e4541dff648f8d972","first_computed_at":"2026-07-05T08:12:46.042362Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:12:46.042362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o8adv9PYKSDqg5oAwxRoIvLIyZm8DAtdrvP2BgSb1SsRzRG8kEXd5t2NFlf3DwVAd/BNhaXicvrnbbvzXRAVBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:12:46.042790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.06243","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:529f0ef138fa66a96c95e706bb2618d6b084d12120cdd25a02b8143ef2434563","sha256:f4a4e0ab2c58ceaad1fde795e5e4796a08072068fd429f93864facc01613e855"],"state_sha256":"f7e62df0654dee660dc435b5affd844952a5df98bade485730a041b2d2926770"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Pbsc1qEyIbIgjFKVEZ7diR+ox6jyBWs79Aw1ekz4WI6dTZi04pTUvTq37Zev2gBrv+Q5aiV2OUAJp1ElMvSDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:36:03.650406Z","bundle_sha256":"b8cf1f3ff5ca03ce41f35283909368b38fc4af44352d1b689adc78a880cce6cb"}}