{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZUVKOSQO56RXJ64YOMG3OP5BGX","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":"92536fbd85a66693792fd54115369550e52cc33b7526348ee9bcb6a0ccbb3d3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T06:33:48Z","title_canon_sha256":"70e585368b1b7c5713e100ab56e8fcdf12d6c99782a4c7e479758cb2bcfdc24f"},"schema_version":"1.0","source":{"id":"2405.13383","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13383","created_at":"2026-07-05T08:44:54Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13383v3","created_at":"2026-07-05T08:44:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13383","created_at":"2026-07-05T08:44:54Z"},{"alias_kind":"pith_short_12","alias_value":"ZUVKOSQO56RX","created_at":"2026-07-05T08:44:54Z"},{"alias_kind":"pith_short_16","alias_value":"ZUVKOSQO56RXJ64Y","created_at":"2026-07-05T08:44:54Z"},{"alias_kind":"pith_short_8","alias_value":"ZUVKOSQO","created_at":"2026-07-05T08:44:54Z"}],"graph_snapshots":[{"event_id":"sha256:ad41edf7d40139f76813a462e60bb2e1b397fd853479d51af7a7979c1ef1ca35","target":"graph","created_at":"2026-07-05T08:44:54Z","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/2405.13383/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, leading to zero-shot generalization collapse, and cross-modal hallucination. In this paper, we reformulate Adapter, LoRA, Prefix-tuning, and Prompt-tuning from the perspective of gradient projection, and firstly propose a unified framework called Parameter Efficient Gradient Projection (PEGP). We introduce orthogonal gradient projection into","authors_text":"Jingyang Qiao, Wensheng Zhang, Xin Tan, Yanyun Qu, Yuan Xie, Zhi Han, Zhizhong Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T06:33:48Z","title":"Gradient Projection For Continual Parameter-Efficient Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13383","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":"sha256:550bfe863c1ea168ea03c4e79d74c035d89ca88ae4a45ce30edd39a00cd3afbd","target":"record","created_at":"2026-07-05T08:44:54Z","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":"92536fbd85a66693792fd54115369550e52cc33b7526348ee9bcb6a0ccbb3d3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-05-22T06:33:48Z","title_canon_sha256":"70e585368b1b7c5713e100ab56e8fcdf12d6c99782a4c7e479758cb2bcfdc24f"},"schema_version":"1.0","source":{"id":"2405.13383","kind":"arxiv","version":3}},"canonical_sha256":"cd2aa74a0eefa374fb98730db73fa135d0343d574b1d9477efe9f5d1374d4af8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd2aa74a0eefa374fb98730db73fa135d0343d574b1d9477efe9f5d1374d4af8","first_computed_at":"2026-07-05T08:44:54.696710Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:54.696710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AWpZak0gYYpktIMHaqQzkI1q56SmerCW7Ji2SsuOhFpGiJtUoycMrezfpkhtgY9QdW6/voETyr5ytKHhlsFsAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:54.697151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.13383","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:550bfe863c1ea168ea03c4e79d74c035d89ca88ae4a45ce30edd39a00cd3afbd","sha256:ad41edf7d40139f76813a462e60bb2e1b397fd853479d51af7a7979c1ef1ca35"],"state_sha256":"a279cbc4b73941bbfff585f18009ce1b4366b65c66881dc009db306c5bc03c29"}