{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:A23YE7P6EFT5HAQQZRYPEVFPDM","short_pith_number":"pith:A23YE7P6","schema_version":"1.0","canonical_sha256":"06b7827dfe2167d38210cc70f254af1b0accbf8418d628f7f6ff343a94c208de","source":{"kind":"arxiv","id":"2403.19522","version":2},"attestation_state":"computed","paper":{"title":"Model Stock: All we need is just a few fine-tuned models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Dong-Hwan Jang, Dongyoon Han, Sangdoo Yun","submitted_at":"2024-03-28T15:57:20Z","abstract_excerpt":"This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from traditional practices that need a multitude of fine-tuned models for averaging, our approach employs significantly fewer models to achieve final weights yet yield superior accuracy. Drawing from key insights in the weight space of fine-tuned weights, we uncover a strong link between the performance and proximity to the center of weight space. Based on this, we introduce a method that approximates a center-close we"},"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":"2403.19522","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-28T15:57:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5d68ca4f3d27c57a0c91cc3db29e19da1724db9fd6117b0bfad33189f5f639dc","abstract_canon_sha256":"c333e0f54ae74b7e629c0cbec75ae6f1c0ab163d33a60903dfb8b2ecb345c241"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:34.350548Z","signature_b64":"VokTDiyWQpBW0swQjiNHxtNSPPXr6+eb4VQYAAdGkgKVSxJjPHWa56ptyFie16bNN+TR6Meuc1VQb+XDhMAlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06b7827dfe2167d38210cc70f254af1b0accbf8418d628f7f6ff343a94c208de","last_reissued_at":"2026-07-05T11:46:34.350038Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:34.350038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model Stock: All we need is just a few fine-tuned models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Dong-Hwan Jang, Dongyoon Han, Sangdoo Yun","submitted_at":"2024-03-28T15:57:20Z","abstract_excerpt":"This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from traditional practices that need a multitude of fine-tuned models for averaging, our approach employs significantly fewer models to achieve final weights yet yield superior accuracy. Drawing from key insights in the weight space of fine-tuned weights, we uncover a strong link between the performance and proximity to the center of weight space. Based on this, we introduce a method that approximates a center-close we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19522","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/2403.19522/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":"2403.19522","created_at":"2026-07-05T11:46:34.350104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.19522v2","created_at":"2026-07-05T11:46:34.350104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19522","created_at":"2026-07-05T11:46:34.350104+00:00"},{"alias_kind":"pith_short_12","alias_value":"A23YE7P6EFT5","created_at":"2026-07-05T11:46:34.350104+00:00"},{"alias_kind":"pith_short_16","alias_value":"A23YE7P6EFT5HAQQ","created_at":"2026-07-05T11:46:34.350104+00:00"},{"alias_kind":"pith_short_8","alias_value":"A23YE7P6","created_at":"2026-07-05T11:46:34.350104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.01674","citing_title":"Can Heterogeneous Language Models Be Fused?","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2408.07666","citing_title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","ref_index":93,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM","json":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM.json","graph_json":"https://pith.science/api/pith-number/A23YE7P6EFT5HAQQZRYPEVFPDM/graph.json","events_json":"https://pith.science/api/pith-number/A23YE7P6EFT5HAQQZRYPEVFPDM/events.json","paper":"https://pith.science/paper/A23YE7P6"},"agent_actions":{"view_html":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM","download_json":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM.json","view_paper":"https://pith.science/paper/A23YE7P6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.19522&json=true","fetch_graph":"https://pith.science/api/pith-number/A23YE7P6EFT5HAQQZRYPEVFPDM/graph.json","fetch_events":"https://pith.science/api/pith-number/A23YE7P6EFT5HAQQZRYPEVFPDM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM/action/storage_attestation","attest_author":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM/action/author_attestation","sign_citation":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM/action/citation_signature","submit_replication":"https://pith.science/pith/A23YE7P6EFT5HAQQZRYPEVFPDM/action/replication_record"}},"created_at":"2026-07-05T11:46:34.350104+00:00","updated_at":"2026-07-05T11:46:34.350104+00:00"}