{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IHSPOL72KE2M4Y7R75XB7HDIJU","short_pith_number":"pith:IHSPOL72","canonical_record":{"source":{"id":"2411.02813","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-05T05:19:09Z","cross_cats_sorted":[],"title_canon_sha256":"8f371fdb2ddf8f3a5251b1456ab6fed53db3bbc99029af1512415fde393cc0f6","abstract_canon_sha256":"15fa7bf1c702f409582364e4202588919ee562d437b36d4c63d0971d7a172307"},"schema_version":"1.0"},"canonical_sha256":"41e4f72ffa5134ce63f1ff6e1f9c684d1d142b6b81ee66c0f8ebce445ab97d69","source":{"kind":"arxiv","id":"2411.02813","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02813","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02813v3","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02813","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_12","alias_value":"IHSPOL72KE2M","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_16","alias_value":"IHSPOL72KE2M4Y7R","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_8","alias_value":"IHSPOL72","created_at":"2026-05-22T01:03:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IHSPOL72KE2M4Y7R75XB7HDIJU","target":"record","payload":{"canonical_record":{"source":{"id":"2411.02813","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-05T05:19:09Z","cross_cats_sorted":[],"title_canon_sha256":"8f371fdb2ddf8f3a5251b1456ab6fed53db3bbc99029af1512415fde393cc0f6","abstract_canon_sha256":"15fa7bf1c702f409582364e4202588919ee562d437b36d4c63d0971d7a172307"},"schema_version":"1.0"},"canonical_sha256":"41e4f72ffa5134ce63f1ff6e1f9c684d1d142b6b81ee66c0f8ebce445ab97d69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:40.666220Z","signature_b64":"TpEn04WFyYrKAIQw8MQ4Caa4HSZW/PhvRTgVmpx9w3ecuV3G7Hft348rmkozENI15a3qlHH8bJcd0b6Fri78Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41e4f72ffa5134ce63f1ff6e1f9c684d1d142b6b81ee66c0f8ebce445ab97d69","last_reissued_at":"2026-05-22T01:03:40.665285Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:40.665285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.02813","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-22T01:03:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DamGn1E3mrtUtx4pSr5o3M1Tc6XXJ/a+/q8kXrBVb8kkBnZiPliuYRyRqwvozHN5G+YNshFhsJS4PaSmzr/JBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:25:57.661441Z"},"content_sha256":"de793695274bba8c4fc286e158a6ed94144821c8aa84caf70c06bef4489aa191","schema_version":"1.0","event_id":"sha256:de793695274bba8c4fc286e158a6ed94144821c8aa84caf70c06bef4489aa191"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IHSPOL72KE2M4Y7R75XB7HDIJU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparse Orthogonal Parameters Tuning for Continual Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hai-Jian Ke, Jia-Yu Yao, Kun-Peng Ning, Li Yuan, Yong-Hong Tian, Yu-Yang Liu","submitted_at":"2024-11-05T05:19:09Z","abstract_excerpt":"Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. These methods typically refrain from updating the pre-trained parameters and instead employ additional adapters, prompts, and classifiers. In this paper, we from a novel perspective investigate the benefit of sparse orthogonal parameters for continual learning. We found that merging sparse orthogonality of models learned from multiple streaming tasks has great potential in addressing catastrophic forgetting. Leveraging this insi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02813","kind":"arxiv","version":3},"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/2411.02813/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":"2026-05-22T01:03:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rsc7M8xMoXFpN03qY+eFJicyai9CdAUzvVyrWEo8AZEol4eoLJl+o/zhYZdtG/o+baMySU5/3P0xLvEbsXJNDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:25:57.661847Z"},"content_sha256":"a1a70edff9146c310ce285c8a5d8d2990847b70d7b12adc6d3bb2097b03a8fdf","schema_version":"1.0","event_id":"sha256:a1a70edff9146c310ce285c8a5d8d2990847b70d7b12adc6d3bb2097b03a8fdf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/bundle.json","state_url":"https://pith.science/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/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":"2026-05-27T00:25:57Z","links":{"resolver":"https://pith.science/pith/IHSPOL72KE2M4Y7R75XB7HDIJU","bundle":"https://pith.science/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/bundle.json","state":"https://pith.science/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IHSPOL72KE2M4Y7R75XB7HDIJU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IHSPOL72KE2M4Y7R75XB7HDIJU","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":"15fa7bf1c702f409582364e4202588919ee562d437b36d4c63d0971d7a172307","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-05T05:19:09Z","title_canon_sha256":"8f371fdb2ddf8f3a5251b1456ab6fed53db3bbc99029af1512415fde393cc0f6"},"schema_version":"1.0","source":{"id":"2411.02813","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02813","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02813v3","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02813","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_12","alias_value":"IHSPOL72KE2M","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_16","alias_value":"IHSPOL72KE2M4Y7R","created_at":"2026-05-22T01:03:40Z"},{"alias_kind":"pith_short_8","alias_value":"IHSPOL72","created_at":"2026-05-22T01:03:40Z"}],"graph_snapshots":[{"event_id":"sha256:a1a70edff9146c310ce285c8a5d8d2990847b70d7b12adc6d3bb2097b03a8fdf","target":"graph","created_at":"2026-05-22T01:03:40Z","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/2411.02813/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catastrophic forgetting. These methods typically refrain from updating the pre-trained parameters and instead employ additional adapters, prompts, and classifiers. In this paper, we from a novel perspective investigate the benefit of sparse orthogonal parameters for continual learning. We found that merging sparse orthogonality of models learned from multiple streaming tasks has great potential in addressing catastrophic forgetting. Leveraging this insi","authors_text":"Hai-Jian Ke, Jia-Yu Yao, Kun-Peng Ning, Li Yuan, Yong-Hong Tian, Yu-Yang Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-05T05:19:09Z","title":"Sparse Orthogonal Parameters Tuning for Continual Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02813","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:de793695274bba8c4fc286e158a6ed94144821c8aa84caf70c06bef4489aa191","target":"record","created_at":"2026-05-22T01:03:40Z","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":"15fa7bf1c702f409582364e4202588919ee562d437b36d4c63d0971d7a172307","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-05T05:19:09Z","title_canon_sha256":"8f371fdb2ddf8f3a5251b1456ab6fed53db3bbc99029af1512415fde393cc0f6"},"schema_version":"1.0","source":{"id":"2411.02813","kind":"arxiv","version":3}},"canonical_sha256":"41e4f72ffa5134ce63f1ff6e1f9c684d1d142b6b81ee66c0f8ebce445ab97d69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"41e4f72ffa5134ce63f1ff6e1f9c684d1d142b6b81ee66c0f8ebce445ab97d69","first_computed_at":"2026-05-22T01:03:40.665285Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:03:40.665285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TpEn04WFyYrKAIQw8MQ4Caa4HSZW/PhvRTgVmpx9w3ecuV3G7Hft348rmkozENI15a3qlHH8bJcd0b6Fri78Cg==","signature_status":"signed_v1","signed_at":"2026-05-22T01:03:40.666220Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.02813","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:de793695274bba8c4fc286e158a6ed94144821c8aa84caf70c06bef4489aa191","sha256:a1a70edff9146c310ce285c8a5d8d2990847b70d7b12adc6d3bb2097b03a8fdf"],"state_sha256":"4f5a519cdd55d6badee17bdf314dfbe03e548ed842e7bba535015333c0ad18f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AlNAGgess5Sa6nOii+Y2EQCKBxvEL1AK3lLmBTOOunagXBgZ+vLr0b3CkGrqcSWQsnrgIR43Q8FRvMuETwo3CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T00:25:57.664880Z","bundle_sha256":"06c60564d5ad2ede0df5ab006163f1517eb0efaf64aceb92fcf6413b80beb1ed"}}