{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OBYLUEDW73CVXMTZDFGW66NFJZ","short_pith_number":"pith:OBYLUEDW","schema_version":"1.0","canonical_sha256":"7070ba1076fec55bb279194d6f79a54e6c99dbb2576c4ed5d9e7dc6b3e1d59dc","source":{"kind":"arxiv","id":"2502.15895","version":2},"attestation_state":"computed","paper":{"title":"Directional Gradient Projection for Robust Fine-Tuning of Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Brisa Maneechotesuwan, Chengyue Huang, Junjiao Tian, Shivang Chopra, Zsolt Kira","submitted_at":"2025-02-21T19:31:55Z","abstract_excerpt":"Robust fine-tuning aims to adapt large foundation models to downstream tasks while preserving their robustness to distribution shifts. Existing methods primarily focus on constraining and projecting current model towards the pre-trained initialization based on the magnitudes between fine-tuned and pre-trained weights, which often require extensive hyper-parameter tuning and can sometimes result in underfitting. In this work, we propose Directional Gradient Projection (DiGraP), a novel layer-wise trainable method that incorporates directional information from gradients to bridge regularization "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.15895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-21T19:31:55Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"a099d34ec2f7a26f34b5c011280391ced71b020d560c25e77a296ad21f678455","abstract_canon_sha256":"312cc66fb4704cbb542f9bd5fd56bc4953d3076916de6bc85d6814ce64833240"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:08.242893Z","signature_b64":"xFgnZlTGAqqXbOg7VPTnf96AB8iKlNAfrzT8gLnDXiqcRO1ALn2FvKDMOfggSpF8pbmujGTr0uwqDZJs6JWiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7070ba1076fec55bb279194d6f79a54e6c99dbb2576c4ed5d9e7dc6b3e1d59dc","last_reissued_at":"2026-07-05T11:25:08.242385Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:08.242385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Directional Gradient Projection for Robust Fine-Tuning of Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Brisa Maneechotesuwan, Chengyue Huang, Junjiao Tian, Shivang Chopra, Zsolt Kira","submitted_at":"2025-02-21T19:31:55Z","abstract_excerpt":"Robust fine-tuning aims to adapt large foundation models to downstream tasks while preserving their robustness to distribution shifts. Existing methods primarily focus on constraining and projecting current model towards the pre-trained initialization based on the magnitudes between fine-tuned and pre-trained weights, which often require extensive hyper-parameter tuning and can sometimes result in underfitting. In this work, we propose Directional Gradient Projection (DiGraP), a novel layer-wise trainable method that incorporates directional information from gradients to bridge regularization "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15895","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/2502.15895/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.15895","created_at":"2026-07-05T11:25:08.242444+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.15895v2","created_at":"2026-07-05T11:25:08.242444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15895","created_at":"2026-07-05T11:25:08.242444+00:00"},{"alias_kind":"pith_short_12","alias_value":"OBYLUEDW73CV","created_at":"2026-07-05T11:25:08.242444+00:00"},{"alias_kind":"pith_short_16","alias_value":"OBYLUEDW73CVXMTZ","created_at":"2026-07-05T11:25:08.242444+00:00"},{"alias_kind":"pith_short_8","alias_value":"OBYLUEDW","created_at":"2026-07-05T11:25:08.242444+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.06795","citing_title":"Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ","json":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ.json","graph_json":"https://pith.science/api/pith-number/OBYLUEDW73CVXMTZDFGW66NFJZ/graph.json","events_json":"https://pith.science/api/pith-number/OBYLUEDW73CVXMTZDFGW66NFJZ/events.json","paper":"https://pith.science/paper/OBYLUEDW"},"agent_actions":{"view_html":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ","download_json":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ.json","view_paper":"https://pith.science/paper/OBYLUEDW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.15895&json=true","fetch_graph":"https://pith.science/api/pith-number/OBYLUEDW73CVXMTZDFGW66NFJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/OBYLUEDW73CVXMTZDFGW66NFJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ/action/storage_attestation","attest_author":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ/action/author_attestation","sign_citation":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ/action/citation_signature","submit_replication":"https://pith.science/pith/OBYLUEDW73CVXMTZDFGW66NFJZ/action/replication_record"}},"created_at":"2026-07-05T11:25:08.242444+00:00","updated_at":"2026-07-05T11:25:08.242444+00:00"}