{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YP7JJRALQYU64F3DZMLEQIMORX","short_pith_number":"pith:YP7JJRAL","schema_version":"1.0","canonical_sha256":"c3fe94c40b8629ee1763cb1648218e8dc43fa7dfc58e9608b13b797ca597ae04","source":{"kind":"arxiv","id":"2501.04288","version":1},"attestation_state":"computed","paper":{"title":"An Analysis of Model Robustness across Concurrent Distribution Shifts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hyoje Lee, Myeongho Jeon, Suhwan Choi, Teresa Yeo","submitted_at":"2025-01-08T05:27:16Z","abstract_excerpt":"Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occur in more complex forms in real-world scenarios, we broadened our study to include multiple concurrent shifts, such as unseen domain shifts combined with spurious correlations. We evaluated 26 algorithms that range from simple heuristic augmentations to zero-shot inference using foundation models, across 168 source-target pairs from eight da"},"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":"2501.04288","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-08T05:27:16Z","cross_cats_sorted":[],"title_canon_sha256":"d8f300e44d9c8d12051c380d0bec7824cf25fa4136c4b19e84b5c2e8f9da9d40","abstract_canon_sha256":"00791b4759db8b8f606f9406aa758512ce3a60b27f20a008e288764e0fe51281"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:32.781445Z","signature_b64":"yC6oVk/600HgbT2LDbk+G58E343rspRFT1YZtqkoo1KSFSL6GzG3tA3PeCoZGSY+8XiV7pEiBZJmA6v/xs25Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3fe94c40b8629ee1763cb1648218e8dc43fa7dfc58e9608b13b797ca597ae04","last_reissued_at":"2026-07-05T09:58:32.780965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:32.780965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Analysis of Model Robustness across Concurrent Distribution Shifts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hyoje Lee, Myeongho Jeon, Suhwan Choi, Teresa Yeo","submitted_at":"2025-01-08T05:27:16Z","abstract_excerpt":"Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occur in more complex forms in real-world scenarios, we broadened our study to include multiple concurrent shifts, such as unseen domain shifts combined with spurious correlations. We evaluated 26 algorithms that range from simple heuristic augmentations to zero-shot inference using foundation models, across 168 source-target pairs from eight da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04288","kind":"arxiv","version":1},"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/2501.04288/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":"2501.04288","created_at":"2026-07-05T09:58:32.781029+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.04288v1","created_at":"2026-07-05T09:58:32.781029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04288","created_at":"2026-07-05T09:58:32.781029+00:00"},{"alias_kind":"pith_short_12","alias_value":"YP7JJRALQYU6","created_at":"2026-07-05T09:58:32.781029+00:00"},{"alias_kind":"pith_short_16","alias_value":"YP7JJRALQYU64F3D","created_at":"2026-07-05T09:58:32.781029+00:00"},{"alias_kind":"pith_short_8","alias_value":"YP7JJRAL","created_at":"2026-07-05T09:58:32.781029+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX","json":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX.json","graph_json":"https://pith.science/api/pith-number/YP7JJRALQYU64F3DZMLEQIMORX/graph.json","events_json":"https://pith.science/api/pith-number/YP7JJRALQYU64F3DZMLEQIMORX/events.json","paper":"https://pith.science/paper/YP7JJRAL"},"agent_actions":{"view_html":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX","download_json":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX.json","view_paper":"https://pith.science/paper/YP7JJRAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.04288&json=true","fetch_graph":"https://pith.science/api/pith-number/YP7JJRALQYU64F3DZMLEQIMORX/graph.json","fetch_events":"https://pith.science/api/pith-number/YP7JJRALQYU64F3DZMLEQIMORX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX/action/storage_attestation","attest_author":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX/action/author_attestation","sign_citation":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX/action/citation_signature","submit_replication":"https://pith.science/pith/YP7JJRALQYU64F3DZMLEQIMORX/action/replication_record"}},"created_at":"2026-07-05T09:58:32.781029+00:00","updated_at":"2026-07-05T09:58:32.781029+00:00"}